Volume 5, Issue 5

Spatial Modelling of Maize Lethal Necrosis Disease in Bomet County, Kenya
Original Research
Maize lethal necrosis (MLN) is a disease that attacks maize crops with significant impacts on both food security and nutrition security on smallholder farmers in Kenya. The study used spatial regression analysis to model MLN severity on sampled farm fields in Bomet County, Kenya. The modelling analysis integrated spatial information based on derived crop mask, on-site derived MLN disease severity index at an optimal maize growing season and phenological stage. Relevant ecological variables derived spatially including temperature, rainfall, soil moisture and slope were identified and fed into a spatial regression model. Significant ecological variables were weighted and used as basis for generating spatially explicit MLN severity index map. MLN affected farms have spatial dependence with MLN severity becoming less correlated the further away from each MLN affected farm field. The ecological variables have negative influence on MLN severity except for temperature. Soil moisture, rainfall and slope are the most significant determinants of MLN severity index in Bomet (all

Journal of Geosciences and Geomatics. 2017, 5(5), 251-258. DOI: 10.12691/jgg-5-5-4
Pub. Date: October 14, 2017
13921 Views2483 Downloads
Testing the Potential Application of Simulated Multispectral Data in Discriminating Tree Species in Taita Hills
Original Research
Hyperspectral data is gaining tremendous popularity in mapping tree species in the recent past. This is due to its ability to distinguish between individual tree species. However, its cost is prohibitive especially for developing economies. This paper focuses on the possibility of using the recently launched free optical sensors for mapping tree species in the Ngangao Forest of Taita Hills in Kenya. The AISA Eagle hyperspectral data was used to 10 tree species (six indigenous and four exotic species). The hyperspectral data reflectance was then used to derive Worldview 2 and Sentinel 2 data. A total 2504 training sites were used for the classification AISA Eagle data. The same training sites were used for the classification of the Worldview 2 and Sentinel 2 but after downscaling due to coarse resolution of the two sensors resulting into 638 and 23 training sites respectively. Spectral angle mapper, neural network and support vector machine classification algorithms were tested in this study. The three algorithms resulted into accuracies 49.24%, 79.47% and 80.15% respectively for the AISA Eagle data. However, only neural network algorithm was able to classify Worldview 2 and Sentinel 2 images resulting into overall accuracies of 56.43% and 47.22% with Kappa coefficient of 0.48 and 0.33 respectively.
Journal of Geosciences and Geomatics. 2017, 5(5), 243-250. DOI: 10.12691/jgg-5-5-3
Pub. Date: September 28, 2017
10870 Views2208 Downloads
Flood Vulnerability Mapping of Lokoja Metropolis Using Geographical Information System Techniques
Original Research
Adequate geographic information on flood vulnerability is required to be able to prepare for flood disasters. This study applies Geographical information System Techniques to produce flood vulnerability map of Lokoja metropolis due to its confluence nature and its potential to cause devastating effect to the surrounding communities. This study is aimed at mapping flood vulnerable areas within Lokoja metropolis, for an effective flood disaster management and proper planning. Satellites imageries MODIS of 2011and 2012, SPOT 5 of 2011, location map of Lokoja Metropolis, SRTM DEM, rainfall data, water discharge/gauge data, and GPS coordinates; acquired during field survey were integrated to map areas vulnerable to flooding. In this study Rank Sum method alongside Principal Component Analysis (PCA) is used to calculate the weight of factors that contributed to flooding within Lokoja metropolis. The study is limited to environmental factors such as hydrology, slope, soil type, drainage density, landform and landuse/landcover. Different maps were generated; composite map of the study area, flood extent map, flood plain map, slope map, flow direction map, flow accumulation map, Triangular irregular network, flood vulnerability map and also pie chart showing percentage area impacted, histogram showing the pattern of rainfall within Lokoja metropolis was generated. The approach resulted in four classes of flood vulnerability ranging from not vulnerable, less vulnerable, more vulnerable and most vulnerable areas. The area not vulnerable accounted for 20.25%, less vulnerable area accounted for 34.57%, more vulnerable area accounted for 28.57%, and the most vulnerable area accounted for 16.61%. The study concludes by proffering a number of recommendations aimed at addressing the issue of flooding within Lokoja metropolis. The recommendations includes; construction of levee along areas that are vulnerable to flooding, widening and construction of standard drainages around Lokoja metropolis, dredging of surrounding water bodies to deepen their depth, among others.
Journal of Geosciences and Geomatics. 2017, 5(5), 229-242. DOI: 10.12691/jgg-5-5-2
Pub. Date: September 21, 2017
27454 Views5534 Downloads
Geochemical Characterization of a Stratigraphic Log Bearing Iron Ore in the Sanaga Prospect, Upper Nyong Unit of Ntem Complex, Cameroon
Original Research
The Sanaga prospect in the north of Edea is located in the upper Nyong unit of the Ntem complex in Cameroon. The objective of this study is to use geochemical data trends for major and some trace elements to constrain the origin and/or sources of various constituents in the iron-bearing units as well as assess their economic potentials. The rock samples were collected from a single drill core sampled at various depths. Major elements were analysed using X-ray fluorescence spectrometry after powder digestion following. All data were processed with the aid of XLSTAT. The stratigraphic log described revealed from top to bottom two lithological sequences composed of oxidized formations (oxidized cap and oxidized gneiss), and gneissic formations (magnetite gneiss, magnetite amphibolite gneiss and enriched magnetite amphibolite gneiss successions). Detailed examination showed that quartz and iron oxides are the main minerals present. Bulk geochemical analysis of the oxidized and gneissic formations showed that Fe2O3 and SiO2 are the main constituents (averaging 84.40 wt % and 92.54 wt %, respectively), confirming that quartz and iron oxides are the major mineral phases in both the oxidised and gneissic formations. Al2O3 averages 9.34 wt % and 3.06 wt %, Na2O averages 0.04 wt % and 0.59 wt %, K2O averages 0.26 and 0.53 wt %, and P2O5 0.07 and 0.05 wt %, respectively, in both oxidized and gneissic formations. Concentrations of trace elements in the various lithologies are generally very low (< 100 ppm). Certain correlations of interest in both units include Al2O3 with LOI (r > 0.8), and Zr (r > 0.7); LOI with Zr (r > 0.8). From these data it appears that mineralisation at the Sanaga prospect is restricted to the magnetite gneiss. The high concentration of Al2O3 (average 9.34 wt %) in the oxidized iron formations is partially due to its introduction during recent chemical weathering. The Sanaga iron formations are metamorphosed chemical sediments formed by precipitation of iron and silica from a mixture of seawater and hydrothermal fluids with a significant terrigenous input.
Journal of Geosciences and Geomatics. 2017, 5(5), 218-228. DOI: 10.12691/jgg-5-5-1
Pub. Date: September 19, 2017
15263 Views3319 Downloads1 Likes