Assessment of Ground Water Dynamics and Potential Zones in Urban Areas: A Case Study of Voi Town, Kenya

Water plays a role in sustaining all the biotic elements. Unfortunately, in the recent times with persistent climate change impacts, parts of the world are facing cases of inadequate water causing stress and increased vulnerability among the people. This is the case with urban areas across the globe as their populations keep increasing with little to no attention paid to urban planning that allows sustainable management of resources amidst rapid development. Urban areas are surrounded by high yielding aquifers that have better water services from groundwater. However, the urban sprawl phenomena have limited attempts in assessing ground water potential in urban areas contributing to urban water scarcity. Therefore, the study aims to look at the problem of urban water scarcity, by analyzing the levels and distribution of groundwater in Voi town using remote sensing and GIS techniques, in order to suggest suitable sites for underground water exploration in regard to the overall urban water supply. From the analysis, the results showed that the area majorly has low to potential zones of groundwater. High potential areas were very few and were mostly on the western side of the area. Very low potential zones were seen on the east and north side of the area.

Assessment of Groundwater Potential and Prediction of the Potential Trend up to 2042 Using GIS-Based Model and Remote Sensing Techniques for Kiambu County

Groundwater is one of the important necessary renewable resources of the world. It forms part of the natural water cycle that is present in the underground strata with the principal sources being precipitation and streamflow. Traditionally, information on the potential occurrence of groundwater was obtained using techniques such as drilling, geophysical, geological, hydro-geological and geo-electrical which are time-consuming, costly and lacked full coverage. This study shows that remote sensing and GIS techniques can be utilized to map groundwater potential using a GIS-based model, the Modified DRASTIC Model, which incorporates factors that influence groundwater occurrence. These factors are the surface attributes that infer groundwater potentials and they include geology, soil texture, land use, lithology, landforms, slope steepness, lineaments and drainage systems. A prediction of the groundwater prediction was done by utilizing the MOLUSCE tool, a plugin in Qgis that utilizes ANN, multicriteria evaluation, weights of evidence and LRs algorithms in predicting land changes. The kappa value for prediction was 0.83. The results showed areas in the Southwest region had low to very low potential and the central region had high to very high potential for all the years and there were little changes between the years. The prediction showed that by 2042, the eastern region of Kiambu County will have a decline in groundwater potential.

A modified stochastic model for LS+AR hybrid method and its application in polar motion short-term prediction

Short-term(up to 30 days)predictions of Earth Rotation Parameters(ERPs)such as Polar Motion(PM:PMX and PMY)play an essential role in real-time applications related to high-precision reference frame conversion.Currently,least squares(LS)+auto-regressive(AR)hybrid method is one of the main techniques of PM prediction.Besides,the weighted LS+AR hybrid method performs well for PM short-term prediction.However,the corresponding covariance information of LS fitting residuals deserves further exploration in the AR model.In this study,we have derived a modified stochastic model for the LS+AR hybrid method,namely the weighted LS+weighted AR hybrid method.By using the PM data products of IERS EOP 14 C04,the numerical results indicate that for PM short-term forecasting,the proposed weighted LS+weighted AR hybrid method shows an advantage over both the LS+AR hybrid method and the weighted LS+AR hybrid method.Compared to the mean absolute errors(MAEs)of PMX/PMY sho rt-term prediction of the LS+AR hybrid method and the weighted LS+AR hybrid method,the weighted LS+weighted AR hybrid method shows average improvements of 6.61%/12.08%and 0.24%/11.65%,respectively.Besides,for the slopes of the linear regression lines fitted to the errors of each method,the growth of the prediction error of the proposed method is slower than that of the other two methods.

Improved LS+MAR hybrid method to UT1-UTC ultra-short-term prediction by using first-order-difference UT1-UTC

Accurate ultra-short-term prediction of the Earth rotation parameters(ERP)holds paramount impor-tance for real-time applications,particularly in reference frame conversion.Among them,diurnal rota-tion(UT1-UTC)which cannot be directly estimated through Global Navigation Satellite System(GNSS)techniques,significantly affects the rapid and ultra-rapid orbit determination of GNsS satellites.Pres-ently,the traditional LS(least squares)+AR(autoregressive)and LS+MAR(multivariate autoregressive)hybrid methods stand as primary approaches for UT1-UTC ultra-short-term predictions(1-10 days).The LS+MAR hybrid method relies on the UT1-UTC and LOD(length of day)series.However,the correlation between LOD and first-order-difference UT1-UTC is stronger than that between LOD and UT1-UTC.In light of this,and with the aid of the first-order-difference UT1-UTC,we propose an enhanced LS+MAR hybrid method to UT1-UTC ultra-short-term prediction.By using the UT1-UTC and LOD data series of the IERS(International Earth Rotation and Reference Systems Service)EOP 14 C04 product,we conducted a thorough analysis and evaluation of the improved method's prediction performance compared to the traditional LS+AR and LS+MAR hybrid methods.According to the numerical results over more than 210 days,they demonstrate that,when considering the correlation information between the LoD and the first-order-difference UT1-UTC,the mean absolute errors(MAEs)of the improved LS+MAR hybrid method range from 21 to 934μs in 1-10 days predictions.In comparison to the traditional LS+AR hybrid method,the MAEs show a reduction of 7-53μs in 1-10 days predictions,with corresponding improvement percentages ranging from 1 to 28%.Similarly,when compared to the traditional LS+MAR hybrid method,the MAEs have a reduction of 5-42μs in 1-10 days predictions,with corresponding improvement percentages ranging from 4-20%.Additionally,when aided by GNSS-derived LOD data series,the MAEs of improved LS+MAR hybrid method experience further reduction.

Using GIS and Remote Sensing in Assessment of Water Scarcity in Nakuru County, Kenya

Water is at the core of sustainable development and is critical for socio-economic development, healthy ecosystems and for human survival. This research study has been carried out in Nakuru County, a tropical region in the Rift Valley of Kenya, bounded between latitude 0.28°N and 1.16°S, and longitude 36.27°E and 36.55°E. The objective of the study has been to use GIS and remote sensing in assessment of water scarcity using Land use Land cover area changes, standard precipitation index and crop yields. Landsat satellite images for the year 1985, 1995, 2005 and 2015 were used. Classification was done using maximum likelihood algorithm while classification accuracy assessment entailed the use of confusion matrix method and ground truth data. Post classification change detection results gave percentage cropland areas as 21% in 1985, 29% in 1995, 53% in 2005 and also 53% in 2015. Eleven (11) ground rainfall stations and TRMM satellite rainfall data from 1985 to 2015 has been used to show meteorological drought. Validation of rainfall data done using correlation coefficient (R2) and root mean square (RMS) methods showed that ground rainfall data and TRMM data correlate. Modelling of 3 months SPI for each of the three seasons (MAM, JJA and OND) has been done using interpolation distance weighted method (IDW). 3 months SPI time scales curves gave October 1987 May 1993, and July 2004 as water scarce and dry seasons and were categorized as either Normal, moderately dry, severely dry and extremely dry. Crop yield trends curves showed crop yield decrease in this identified water scarce and dry years. Conclusion reached is that crop yields is not dependent on size of land ploughed only but mostly on rainfall quantities. Therefore, the findings of this research can be used as drought monitoring tools.

Study of Urban Heat Island Trends to Aid in Urban Planning in Nakuru County-Kenya

Urban Heat Island (UHI) is a phenomenon characterized by higher surface and atmospheric temperatures in urbanized areas as compared to the surrounding rural areas. This phenomenon is a consequence of increase in Land Surface Temperatures (LST) as a result of trapped heat energy on the surface. The objective of this study is establishing the trends in and relationship between LST and land use/land cover in Nakuru County as it seeks to achieve the ultimate goal to contain the UHI effect. Urban heat island inference was based on the generation of a time series set of Landsat imagery, with particular emphasis on the thermal band. Land use/land cover mapping was conducted using maximum likelihood classification techniques, and this, like the LST, is generated in a time series fashion from 1989 to 2015. Accuracy assessment was conducted in order to give confidence in the classification results. The accuracy of the development was assessed using observed temperature data as recorded by the ground stations at the Kenya Meteorological Department. This study employed Normalized NDVI and NDBI to investigate the variation land use/land cover. Results revealed that over the years, settlement has been on an upward trend in terms of area whereas forests have been decreasing due to deforestation. Also, the land surface temperatures have been increasing over the years. In order to qualify this, the correlation between LST and Land Use change was conducted and it indicated that changes to settlement/urban increased proportionately with Land Surface Temperature.

Influence of Vegetation Cover on the Oh Soil Moisture Retrieval Model: A Case Study of the Malinda Wetland, Tanzania

Soil moisture is an important parameter that drives agriculture, climate and hydrological systems. In addition, retrieval of soil moisture is important in the analysis as well as its influence on these systems. Radar imagery is best suited for this retrieval due to its all-weather capability and independence from solar irradiation. Soil moisture retrieval was done for the Malinda Wetland, Tanzania, during two time periods, March and September 2013. The aim of this paper was to analyze soil moisture retrieval performance when vegetation contribution is taken into account. Backscatter values were obtained from TerraSAR-X Spotlight mode imagery taken in March and September 2013. The backscatter values recorded by SAR imagery are influenced by vegetation, soil roughness and soil moisture. Thus, in order to obtain the backscatter due to soil moisture, the roughness and vegetation contribution are determined and decoupled from total backscatter. The roughness parameters were obtained from a Digital Surface Model (DSM) from Unmanned Aerial Vehicle (UAV) photographs whereas the vegetation parameter was obtained by inverting the Water Cloud Model (WCM). Lastly, soil moisture was retrieved using the Oh Model. The coefficient of correlation between the observed and retrieved was 0.39 for the month of March and 0.65 in the month of August. When the vegetation contribution was considered, the r2 for March was 0.64 and that in August was 0.74. The results revealed that accounting for vegetation improved soil moisture retrieval.

Subsurface accumulation of CaCO3 and Cl- from groundwater under black locust and poplar plantations

When conditions are similar,more water evaporates from forest plantations than herbaceous vegetation,thereby affecting hydrological fluxes and ion transport in the soil.The vertical distribution of CaCO3 and Cl^-ions shifts due to afforestation.The effect of groundwater depth and clay content were studied in the Great Hungarian Plain where forest area has been increasing for decades by analyzing soil and groundwater samples from stands of black locust(Robinia pseudoacacia,11 plots)and poplar(Populus spp.,11 plots).All study sites contained one herbaceous(control)and one or more forested plots.CaCO3 and Cl^-ions accumulated in the soil profile in greater quantities under tree cover than in the controls.The scale of this process largely depended on the species and on soil and ion properties.Under black locust,Cl^-accumulated between 1.3 and 6.3 m,with a maximum difference of 0.3 pCl unit(pCl is Cl^-activity,the negative of the logarithm to base 10 of the concentration of the chloride ion,determined using an ion-selective electrode,it is a dimensionless quantity.),while the difference in CaCO3 accumulation was at most 3.5%in some layers,compared to control plots.This result may be explained by the difference in the mobility of Ca+and Cl^-ions.Different mechanisms were noticeable under poplar plantations due to their higher water uptake:Cl-accumulation was detected below 0.9 m to the groundwater with a maximum difference of 0.5 pCl units,while CaCO3 accumulation was continuous at depths of 2.3–6.8 m with a maximum difference of 8.4%,compared to the controls.With increasing clay content,there was a discernible effect on CaCO3 and Cl-accumulation under black locust,but not observed under poplars.These differences were explained by the differences in water uptake mechanisms and root patterns of the two species and the different mobility of Ca2^+and Cl-ions.

Harmonization of Political and Administrative Boundaries in Kenya Using GIS: A Case Study of Mavoko Constituency

The objective of this research was to harmonize political and administrative boundaries in Mavoko Constituency, Machakos County, Kenya, using geo-spatial technology and public consultation in order to address the numerous disputes that exist. Specifically, the project focused on analyzing the existing electoral and administrative boundaries in the area of study by identifying all existing gaps and overlaps, rectifying the identified gaps and overlaps and created a web based geo-portal of the harmonized sub location boundaries to enable sharing. This was achieved through first putting the various datasets on the same Spatial Reference System and comparing them in order to identify and rectify them using various geospatial operations, public consultation and field work. The result was a single harmonized layer for the sub location data set for the study area. The project finally developed a customized GIS web based portal for sharing the harmonized Sub location boundary. This was uploaded on an open source platform, which has the capability for offline use in the field. The project worked on a hypothesis that if the sub location boundaries adopted by the various Government and Public organizations and individuals doing any kind of mapping in Kenya was a single harmonized data set, the emerging issues on boundary disputes between Counties, Constituencies and County Assembly Wards (CAW’s) would not occur. This is because there would be authentic, consisted and authoritative geo-information which maintains a harmonized, consistent, reusable and readily available data. The development of the geo portal ensured that the results of the research are easily accessible on www.msc2015boundariesmavoko.info.ke/mavoko.

Development of a Web-Based Geographic Information System for Mass Land Valuation: A Case Study of Westlands Constituency, Nairobi County

The development of a web-based Geographic Information System for mass property valuation was the main focus of this research. The developed web GIS allows effective dissemination, extraction and analysis of mass land valuation information over the Internet. It also allows for automation of the mass property valuation process by compiling a centralized mass valuation roll database. The Westlands Constituency, one of the administrative regions of the Nairobi City County was used as a case study. The research focused on automation of the mass property valuation roll by creating a centralized database that is accessible by all users on the web-based GIS portal. This was done by customizing and integrating a web-based GIS system based on open source QuantumGIS, GeoServer and PostgreSQL/PostGIS as a relational database. Leaflets APIs were used for the development of an interactive and friendly geographic user interface. The developed system enables users to view and interact with the spatial data. This improves the efficiency and effectiveness of the decision making process and data sharing for mass property valuation and optimal property taxation purposes.

Spatial Effects of Varying Model Coefficients in Urban Growth Modeling in Nairobi, Kenya

Urban land-use modeling has gained increased attention as a research topic over the last decade. This has been attributed to advances in remote sensing and computing technology that now can process several models simultaneously at regional and local levels. In this research we implemented a cellular automata (CA) urban growth model (UGM) integrated in the XULU modeling frame-work (eXtendable Unified Land Use Modeling Platform). We used multi-temporal Landsat satellite image sets for 1986, 2000 and 2010 to map urban land-use in Nairobi. We also tested the spatial effects of varying model coefficients. This approach improved model performance and aided in understanding the particular urban land-use system dynamics operating in our Nairobi study area. The UGM was calibrated for Nairobi and predicted development was derived for the city for the year 2030 when Kenya plans to attain Vision 2030. Observed land-use changes in urban areas were compared to the results of UGM modeling for the year 2010. The results indicate that varying the UGM model coefficients simulates urban growth in different directions and magnitudes. This approach is useful to planners and policy makers because the model outputs can identify specific areas within the urban complex which will require infrastructure and amenities in order to realize sustainable development.

Mapping of Freshwater Snails’ Habitat—A Source of Transmitting Bilharzia in Mwea Sub-County, Kenya

Bilharzia is vector-borne disease carried by a parasite that is hosted by fresh water snails. The distribution of the disease is concurrent with the existence of the freshwater snails and is dependent on certain suitable environmental conditions. It is difficult to identify the specific habitats of the snails as they are often inaccessible on the ground, the snails also migrate by means of flowing water, making it difficult to keep a track of the freshwater snails’ habitat. This paper aimed at using GIS, Remote Sensing and Species Distribution Modelling techniques to model the suitable habitats for the freshwater snails and to prove that the snails migrate when there are sudden changes in water levels whilst showing the population at risk of bilharzia. The SDM used is the Maximum Entropy (MAXENT) for its ability to make right predictions even with small presence sites. The AUC value of the model was 0.951. The research results showed that the environmental variables;brightness Index, elevation, temperatures were negatively correlated with the snails’ presence while the wetness index, MSAVI, greenness index and soil pH were positively correlated. The snails are observed to favor clay soils of the montmorillonite type and the crop-lands land cover. Areas consistently submerged by water especially after flooding are shown to be the most suitable areas where snails migrate by means of river or canal water. The research proves that Mwea is not the source habitat of the freshwater snails. The neighboring sub-counties within Kirinyaga County should be investigated using such models as a likely source-habitat of the freshwater snails. Destroying the source habitats will lead to complete eradication of the freshwater snails within Mwea.

A Web-Based Pilot Implementation of the Africanized Land Administration Domain Model for Kenya—A Case Study of Nyeri County

Kenya has amassed a wealth of paper based land information records collected over the duration of more than a century. The National Land Commission (NLC) having the mandate to develop a National Land Information Management System (NLIMS) for Kenya partnered with the Dedan Kimathi University of Technology on a project to develop a pilot LIMS for Nyeri County. A pilot Land Administration System (LAS) has been developed in this work and utilizes an Africanized Land Administration Domain Model (A-LADM) fitted to the Kenyan context. Various processes involved in land administration that required to be automated were identified. Informed by the numbers of applications made for the change of User service, it was picked as the first workflow to be automated. The key outputs of this work were the A-LADM and pilot LAS. The pilot solution uses a webcentric solution, with the data stored and managed centrally from a PostGIS database backend, using the Python Django framework to implement the server side and client side frontend. This solution demonstrates the importance of automating processes and supporting standards based software development. Stakeholder participation is key when implementing systems and 2 workshops are held to capture requirements and validate the developed solution.

Identification and Mapping of Essential Fish Habitats Using Remote Sensing and GIS on Lake Victoria, Kenya

Fisheries in Lake Victoria have been threatened by declining fish stocks and diversity, environmental degradation due to increased input of pollutants, industrial and municipal waste, overfishing and use of unapproved fishing methods, infestation by aquatic weeds especially water hyacinth, de-oxygenation and a reduction in the quantity and quality of water. Remote sensing and GIS are essential tools in detection of fishing grounds which is important in providing fish sustainability for human beings and allows fishing grounds detection at minimal cost and optimizes effort. This research tends to identify the most favorable both environmentally and ecologically satisfactory factors which favor fish breeding and growth. The main aim of the study was to identify habitat variables that promote fish breeding and growth to maturity including the extraction of environmental variables from Landsat 8 images for the study period and using suitability index derived from fishery data. The study concentrated on establishing suitability ratings in different parts of Lake Victoria using lake surface temperature and chlorophyll-a levels. The study was conducted for months;January, May and December 2019 on Lake Victoria (limited by the availability of recent data). The factors were analysed and the favorable regions mapped satisfying the conditions for fish breeding. The output obtained illustrated the availability of suitable and habitable zones within the lake using satellite imagery and the suitability index. The fish catch data and satellite derived variables were used to determine habitat suitability indices for fish during January, May and December 2019. More than 90% of the total catch was found to come from the areas with sea surface temperature of 23.0˚C - 28.3˚C and chlorophyll-a concentration between 0.72 - 1.31 mg/m3. The catch data was used to validate the images. This study indicated the capability of High Satellite Resolution Imageries (HSI) as a tool to map the potential fishing grounds of fish species in Lake Victoria. The variables were affected by climatic change factors like rainfall and temperature of the lake basin and other human activities around the lake and also the species ecosystem like competition or predation.

Low-cost cloud computing solution for geo-information processing

Cloud computing has emerged as a leading computing paradigm,with an increasing number of geographic information(geo-information) processing tasks now running on clouds.For this reason,geographic information system/remote sensing(GIS/RS) researchers rent more public clouds or establish more private clouds.However,a large proportion of these clouds are found to be underutilized,since users do not deal with big data every day.The low usage of cloud resources violates the original intention of cloud computing,which is to save resources by improving usage.In this work,a low-cost cloud computing solution was proposed for geo-information processing,especially for temporary processing tasks.The proposed solution adopted a hosted architecture and can be realized based on ordinary computers in a common GIS/RS laboratory.The usefulness and effectiveness of the proposed solution was demonstrated by using big data simplification as a case study.Compared to commercial public clouds and dedicated private clouds,the proposed solution is more low-cost and resource-saving,and is more suitable for GIS/RS applications.

Geo-Hazard Susceptibility Assessment and Its Impacts on Livelihoods in Kerio Valley, Kenya

Geohazards are a recurrent issue in the Kerio River catchment of Kenya, which usually results in life and property loss. This research focuses on mapping geo-hazard risk zones of the region. The risk zones were developed from a combination of land use land cover maps, agroecological zones maps and soil erosion maps using the Analytical Hierarchy Process method of multi-criteria analysis. The final results depict the geohazard risk maps which show the susceptibility of different areas in the catchment (classified as risk zones) to hazards. The zones range from no risk zones to very high-risk zones. The results showed that the lowlands are most susceptible to hazards as they were classified as high-risk zones. These risk zone areas have impacts on the socio-economic development hence negatively impacting livelihoods in the area.

Developing a Web-Based Water Distribution Geospatial Information System for Nairobi Northern Region

This research aimed at developing a web system that will allow effective dissemination, extraction and analysis of water utilities information over the internet. The northern region, one of the administrative regions of the Nairobi City Water and Sewerage Company was used as a case study. The research has customized and integrated an open source WebGIS system based on Quantum GIS for spatial data creation, MapServer as a web GIS server and PostgreSQL/PostGIS as a relational database. GeoMOOSE was used for the development of an interactive and friendly geographic user interface. The developed system enables users to view and interact with the spatial data. The research focused on improving the efficiency and effectiveness of the decision making process and data sharing.

Crop phenotyping studies with application to crop monitoring

1.Introduction Crop yield must urgently be sustainably increased to accommodate a rising global population and anticipated climate change in the coming decades,in the face of plant stresses and limited resources[1].Conventional crop breeding is limited by phenotypic selection and breeding efficiency.

Spatio-Temporal Drought Characterization in Kenya from 1987 to 2016

Kenya is a drought, famine and hunger prone country, with considerable impact on agriculture, human health and livestock due to its eco-climatic conditions. It contains only a few regions of high and regular rainfall where arid and semi-arid lands cover 80% of the territory, therefore periodical droughts are part of the climate system. Some drought studies undertaken in Kenya used Standardized Precipitation Index (SPI) which could not fully account for drought severity status as the role of temperature increase on drought conditions was not taken into account. This study has tried to fill the gap by using Standardized Precipitation Evapotranspiration Index (SPEI), which includes precipitation, a temperature component and evapotranspiration in its computations. SPEI and the Normalized Difference Vegetation Index Anomaly (NDVI) were applied to characterize drought in Kenya from 1987 to 2016, investigate the drought severity and duration in the same period, assess drought trends together with mapping of spatial distribution of drought in identified months, assessment of Agricultural, meteorological and socio-economic activities. Correlation analysis was done to understand the response of climate and satellite based drought monitoring indices results and the crop yield data. The results and analysis obtained from the study showed that the years 1987, 1998, 2000, 2001, 2005, 2006, 2008, 2009, 2010, 2011 and 2015 were considered as drought years based on their SPEI and NDVI anomaly results. They were classified as extremely dry, very dry and moderately dry for meteorological drought and slight, moderate, severe and very severe for Agricultural drought. SPEI results can be rated as being superior as the element of temperature variation is taken into consideration.

Seasonal Vegetation Changes in the Malinda Wetland Using Bi-Temporal, Multi-Sensor, Very High Resolution Remote Sensing Data Sets

Small wetlands in East Africa have grown in prominence driven by the unreliable and diminished rains and the increasing population pressure. Due to their size (less than 500 Ha), these wetlands have not been studied extensively using satellite remote sensing approaches. High spatial resolution remote sensing approaches overcome this limitation allowing detailed inventorying and research on such small wetlands. For understanding the seasonal variations in land cover within the Malinda Wetland in Tanzania (350 Ha), two periods were considered, May 2012 coinciding with the wet period (rainy season) and August 2012 coinciding with a fairly rain depressed period (substantially dry but generally cooler season). The wetland was studied using very high spatial resolution orthophotos derived from Unmanned Aerial Vehicle (UAV) photography fused with TerraSAR-X Spotlight mode dual polarized radar data. Using these fused datasets, five main classes were identified that were used to firstly delineate seasonal changes in land use activities and secondly used in determining phenology changes. Combining fuzzy maximum likelihood classification, knowledge classifier and Change Vector Analysis (CVA), land cover classification was undertaken for both seasons. From the results, manifold anthropogenic activities are taking place between the seasons as evidenced by the high conversion rates (63.01 Ha). The phenological change was also highest within the human influence class due to the growing process of cropped land (26.60 Ha). Much of the changes in both cover and phenology are occurring in the mid upper portion of the wetland, attributed to the presence of springs in this portion of the wetland along the banks of River Mkomazi. There is thus seasonality in the observed anthropogenic influence between the wetland and its periphery.