Mass Valuation of Unimproved Land Value Case Study: Nairobi County

The purpose of this study is to investigate mass valuation of unimproved land value using machine learning techniques. The study was conducted in Nairobi County. It is one of the 47 Kenyan Counties under the 2010 constitution. A total of 1440 geocoded data points containing the market selling price of vacant land in Nairobi were web scraped from major property listing websites. These data points were adopted as dependent variables given as unit price of vacant land per square meter. The Covariates used in this study were categorized into Accessibility, Environmental, Physical and Socio-Economic Factors. Due to multi-collinearity problem present in the covariates, PLS and PCA methods were adopted to transform the observed features using a set of vectors. These methods resulted in an uncorrelated set of components that were used in training machine learning algorithms. The dependent variable and uncorrelated components derived feature reduction methods were used as training data for training different machine learning regression models namely;Random forest, support vector regression and extreme gradient boosting regression (XGboost regression). PLS performed better than PCA because the former maximizes the covariance between dependent and independent variables while the latter maximizes variance between the independent variables only and ignores the relationship between predictors and response. The first 9 components were identified as significant both by PLS and PCA methods. The spatial distribution of vacant land value within Nairobi County was consistent for all the three machine learning models. It was also noted that the land value pattern was higher in the central business district and the pattern spread northwards and westwards relative to the CBD. A relative low vacant land value pattern was observed on the eastern side of the county and also at the extreme periphery of Nairobi County boundary. From the accuracy metrics of R-squared and MAPE, Random Forest Regression model performed better than XGBoost and SVR models. This confirms the capability of random forest model to predict valid estimates of vacant land value for purposes of property taxation in Nairobi County.

Influence of Temperature and Frequency on Microwave Dielectric Properties of Lunar Regolith Simulant

The dielectric constant of the lunar regolith can directly influence the reflection coefficient and the trans-mission coefficient of the Moon′s surface, and plays an important role in the Moon research. In order to study the di-electric properties of the lunar regolith, the lunar regolith simulant was made according to the making procedure of the CAS-1 simulant made by Chinese Academy of Sciences. Then the dielectric constants of the lunar regolith simulant were measured with 85070E Aiglent Microwave Network Analyzer in the frequency ranging from 0.2 GHz to 20.0 GHz and at temperature of 25.1℃, 17.7℃, 13.1℃, 11.5℃, 9.6℃, 8.0℃, 4.1℃, -0.3℃, -4.7℃, -9.5℃, -18.7℃, -27.7℃, and -32.6℃, respectively. The Odelevsky model was employed to remove the influence of water in the air on the final effective dielectric constants. The results indicate that frequency and temperature have apparent influences on the dielectric constants of the lunar regolith simulant. The real parts of the dielectric constants increase fast over the range of 0.2 GHz to 3.0 GHz, but decrease slowly over the range of 4.0 GHz to 20.0 GHz. The opposite phenomenon occurs in the imaginary parts. The influences of the frequency and temperature on the brightness temperature were also estimated based on the radiative transfer equation. The result shows that the variation of the frequency and temperature results in great changes of the microwave brightness temperature emitting from the lunar regolith.

Assessment of Soil Erosion and Climate Variability on Kerio Valley Basin, Kenya

This study was aimed at assessing soil erosion, climate variation and how climate has affected both the agro climatic and agro-ological zones of Kerio Valley basin. The basin faces challenges especially soil loss, due to the massive degradation that takes place in Kerio valley. Due to the increase in rainfall recently experienced in the area, most of the top soil has been carried away leading to excessive degradation of the valley, causing soil loss in the basin and subsequent deposition of the sediments in Lake Kamnarok which is an oxbow lake posing it to the threat of extinction. All these aforementioned factors, i.e. soil erosion, climate variation and land degradation have contributed to reduction of water storage capacity of the Lake. The main objective of this study was to assess the effects of soil erosion, climate variation on the basin and climate effect on agro-climatic and agro-ecological zones of the basin. Agro-climatic zones show how climate variability shapes agricultural landscape of an area while agro-ological zones show how agriculture affects the ecology of the basin. This includes the reduction of the lake size that has led to the disruption of the ecology of Lake Kamnarok and its environs, the major implications being the lake size reduction as the lake is proved to be a home for reptiles especially crocodiles. All these factors were finally assessed to determine their effect on water reduction capacity of Lake Kamnarok. The results depicted that the major factors that have caused changes in the basin and the Lake include heavy rainfall that has resulted in soil erosion and subsequent land degradation. These factors have eventually affected the agroclimatic and agroecological zones of the basin. This study integrated the use of Geographic Information System (GIS) and Remote Sensing (RS) to assess the areas with massive degradation and to quantify the amount of soil loss using Revised Universal Soil Loss Equation (RUSLE) model. It was concluded that the main factor that caused the changes in the agroclimatic and the agroecological zones was soil erosion which was influenced by climatic factors, i.e. rainfall and temperature.

Modeling the Spatial Distribution of Soil Heavy Metals Using Random Forest Model—A Case Study of Nairobi and Thirirka Rivers’ Confluence

Modeling the spatial distribution of soil heavy metals is important in determining the safety of contaminated soils for agricultural use. This study utilized 60 topsoil samples (0 - 30 cm), multispectral images (Sentinel-2), spectral indices, and ancillary data to model the spatial distribution of heavy metals in the soils along the Nairobi River. The model was generated using the Random Forest package in R. Using R2 to assess the prediction accuracy, the Random Forest model generated satisfactory results for all the elements. It also ranked the variables in order of their importance in the overall prediction. Spectral indices were the most important variables within the rankings. From the predicted topsoil maps, there were high concentrations of Cadmium on the easterly end of the river. Cadmium is an impurity in detergents, and this section is in close proximity to the Nairobi water sewerage plant, which could be a direct source of Cadmium. Some farms had Zinc levels which were above the World Health Organization recommended limit. The Random Forest model performed satisfactorily. However, the predictions can be improved further if the spatial resolutions of the various variables are increased and through the addition of more predictor variables.

Mesoscale surface circulation and variability of Southern Indian Ocean derived by combining satellite altimetry and drifter observations

High resoultion Eulerian mean velocity field has been derived by combining the satellite tracked surface drifter data with satellite altimetry and ocean surface winds. The drifter data used in this study includes Argos and surface drifter data from Global Drifter Program. Maps of Sea Level Anomaly （MSLA） weekly files with a resolution of （1/3）° in both Latitude and Longitude for the period 1993-2012 have been used. The Ekman current is computed using ocean surface mean wind fields from scatterometers onboard ERS 1/2, Quikscat and ASCAT. The derived mean velocity field exhibits the broad flow of Antarctic Circumpolar Current with speeds up to 0.6 m/s. Anomalous field is quite significant in the western part between 20~ and 40~E and in the eastern part between 80~E and 100~E with velocity anomaly up to 0.3 m/s. The estimated mean flow pattern well agrees with the dynamic topography derived from in-situ observations. Also, the derived velocity field is consistent with the in-situ ADCP current measurements. Eddy kinetic energy illustrates an increasing trend during 1993-2008 and is in phase coherence with the Southern Annular Mode by three month lag. Periodic modulations are found in the eddy kinetic energy due the low frequency Antarctic Circumpolar Wave propagation.

Determination of Suitable Sites for Establishment of Large-Scale Concentrated Solar Power Plants in Kenya

The demand for energy in Kenya, especially for electricity, is increasing rapidly due to population growth, decentralization of governance, and technological and industrial development. Hydroelectricity, the core source of power, has proved unreliable due to the rapid climate change. In response, the country has ventured into other renewable sources to counter the issues posed by the alternative nonrenewable sources such as unreliability, high costs, and environmental degradation as seen with the use of diesel and kerosene. The purpose of this research is to determine the viability of setting up a large-scale concentrated solar power plantation in Kenya that will assist in stabilizing Kenya’s energy demand and supply as well as increase its affordability. The project is divided into three phases. The first phase conducts an overlay analysis to determine the Kenya’s solar energy potential. The results show that the northern region has the highest potential. The second step involves the creation of an exclusion mask which eliminates the unsuitable land forms and Land Use Land Cover. Based on the results, the best ten sites are situated in Turkana and Marsabit counties. The final phase involves the evaluation of the potential capacity of power that could be generated per square kilometer. The study finds out that the potential varies based on the technologies: parabolic trough, linear Fresnel reflector, or dish systems.

Assessment of health impacts of noise pollution in the Tarkwa Mining Community of Ghana using noise mapping techniques

Objective Communities in the developing countries such as Ghana have little knowledge of effects of noise pollution on human health,which is demonstrated by their attitude towards this menace.This study assessed the health impacts of noise pollution and its spatial distribution in the Tarkwa Mining Community(TMC)of Ghana.Methods To achieve the study objective,questionnaires were administered;as well as collation of health data from major health centers in the study area.Noise levels were measured and noise map produced using geographic information system(GIS)techniques.Overlay maps of some diseases were done using overlay techniques in GIS.The noise exposure and corresponding noise doses for churches,working sites and social centers were also calculated using the respective formulae.Results The noise levels were found to be high above the prescribed Ghana Environmental Protection Agency(EPA)standards,with traffic noise levels ranging from 65.00 dBA to 98 dBA,while that of churches ranged from 73.10 dBA to 107.00 dBA and that of working sites from 74.4 dBA to 115.2 dBA.The calculated noise exposure and corresponding noise dose for churches ranged from 75.1 dBA to 104.6 dBA(i.e.,10%‒8000%),while that for workers’sites were from 75.8 dBA to 115 dBA(i.e.,12%‒90000%).Statistical regression and correlation analyses were done for diseases such as hypertension,ear problems and sleep disturbances.Conclusion The study has therefore revealed that the noise levels in the study area are very high and corresponding health impacts are prominent.Stakeholders and authorities should devise mitigating measures to combat this rising menace.The results revealed a strong positive correlation between noise and corresponding health impacts.Despite the positive correlation there are other causes and effects to the mentioned diseases.

Geospatial Assessment for Sustainable Management of Mangroves in Kilifi Creek, Kenya

Mangroves are salt-tolerable trees that grow on zones parallel to the coastline along the creeks. They follow the mud flat accretions which are unvegetated areas consisting of sand or gravel that are either exposed or flooded by tides. They provide 70% of the wood requirement along the Kenyan Coast. Currently, there are no harvest plans of the mangroves and there is selective removal of suitable poles and most of the quality poles have been wiped out. This not only leaves the inferior species unsuitable for the market but also affects the quality of the forest. Moreover, areas that are suitable for mangroves growth have been occupied by human settlement and infrastructure, hence, there is a need of sustainable use of the mangroves so as to protect them from degradating and eventually extinction. To achieve this, geospatial techniques need to be employed in order to determine the spatial extent of the vegetation and devise methods and plans of managing them. The Kilifi Mangrove Forest creek is home to major six species: Avicennia marina, Ceriops tagal, Sonneratia alba J., Rhizophora mucronata, Lumnitzera racemosa and Bruguiera gymnorrhiza. This study showed that the most dominant species in the forest is Avicenna Marina which had a percentage stand of 25.6%. The less dominant species Lumnitzera racemosa and Heritiera littoralis had a stand of 0.10% which were restricted for harvesting in the analysis, they need to be protected so as to prevent its extinction in the forest which will affect the biodiversity and richness of the forest. Density and heights of the mangroves were considered so as to decide on which areas to do reforestation in order to protect the forest and help in preventing soil erosion. The final suitable area for harvesting after carrying out conditional and majority filter was 394 acres which are 9% of the total forest area. The total area most suitable for reforestation is 1151 acres which are 27% of the total Kilifi Mangrove Forest. A recommendation for proper harvesting plans should be made by identifying suitable sites for harvesting and areas which showed low mangrove stand density should be identified and necessary measures should be taken to restore them.

Spatial Modelling of Current and Future Piped Domestic Water Demand in Athi River Town, Kenya

Water scarcity is currently still a global challenge despite the fact that water sustains life on earth. An understanding of domestic water demand is therefore vital for effective water management. In order to understand and predict future water demand, appropriate mathematical models are needed. The present work used Geographic Information Systems (GIS) based regression models;Geographically weighted regression (GWR) and Ordinary Least Square (OLS) to model domestic water demand in Athi river town. We identified a total of 7 water determinant factors in our study area. From these factors, 4 most significant ones (household size, household income, meter connections and household rooms) were identified using OLS. Further, GWR technique was used to investigate any intrinsic relationship between the factors and water demand occurrence. GWR coefficients values computed were mapped to exhibit the relationship and strength of each explanatory variable to water demand. By comparing OLS and GWR models with both AIC value and R2 value, the results demonstrated GWR model as capable of projecting water demand compared to OLS model. The GWR model was therefore adopted to predict water demand in the year 2022. It revealed domestic water demand in 2017 was estimated at 721,899 m3 compared to 880,769 m3 in 2022, explaining an increase of about 22%. Generally, the results of this study can be used by water resource planners and managers to effectively manage existing water resources and as baseline information for planning a cost-effective and reliable water supply sources to the residents of a town.

Estimation of Above Ground Biomass in Forests Using Alos Palsar Data in Kericho and Aberdare Ranges

Above Ground Biomass is one of the six pools identified in the inventory of forest resources and estimation of greenhouse gas emissions and sinks from the forestry sector. The pool varies by management practices in different agro-ecological or agro-climatic zones in forests. The quantification of above ground biomass (AGB) hence carbon sequestration in forests has been very difficult due to the immense costs required. This research was done to estimate AGB using ALOS PALSAR L band data (HH, HV polarisation) acquired in 2009 in relation with ground measurements data in Kericho and Aberdares ranges in Kenya. Tree data information was obtained from ground measurement of DBH and tree heights in 100 circular plots of 15 m radius, by use of random sampling technique. ALOS PALSAR image is advantageous for its active microwave sensor using L-band frequency to achieve cloud free imageries, and the ability of long wavelength cross-polarization to estimate AGB accurately for tropical forests. The variations result between Natural and plantation forest for measured and estimated biomass in Kericho HV band regression value was 0.880 and HH band was 0.520. In Aberdare ranges HV regression value of 0.708 and HH band regression value of 0.511 for measured and estimated biomass respectively. The variations can be explained by the influence of different management regimes induced human disturbances, forest stand age, density, species composition, and trees diameter distribution. However, further research is required to investigate how strong these factors affect relationship between AGB and Alos Palsar backscatters.

Ecological Niche Modeling of Zebra Species within Laikipia County, Kenya

Wildlife conservation is essential, especially for countries like Kenya which rely on tourism as a major earner of foreign exchange. Conservation of species with minimal ecological information such as Grevy’s zebra, though a challenge, is critical to enable the future survival of such species. Grevy’s and Plains zebra have been classified as endangered and near-threatened by International Union for Conservation of Nature and Natural Resources (IUCN) respectively, with Grevy’s zebra found mostly in Northern Kenya and Ethiopia. This has been due to habitat degradation from livestock grazing, local hunting and development of resorts. Six prediction variables i.e. rainfall, temperature, land use, population, NDVI and cattle occurrence were used in Maxent algorithm to produce a habitat prediction map for both species. Both prediction maps had an AUC > 0.75, which is adequate for conservation planning. Niche similarity based on Warren’s I index (I = 0.78) indicates that both zebra species are identical based on their occupied niche environments, suggesting that similar conversation strategies can be adopted for both species.

Assessing the Suitability of the WorldClim Dataset for Ecological Studies in Southern Kenya

There have been numerous efforts to generate freely available climatic datasets for use in species distribution models, the most popular being the global climatic dataset known as WorldClim. The availability of such datasets is invaluable to scientists as many studies are performed in remote areas where no weather stations are found. However, many users do not critically assess the suitability of these datasets for their applications, and errors associated with global datasets are often assumed to be negligible. Understanding what a climate dataset can or cannot deliver requires the user to have a working knowledge of what the basic spatial climate-forcing factors are at the scale of his/her study, and to have a good understanding of the uncertainty in the dataset. In geographic studies, uncertainty is often described by the degree of error (uncertainty), or degree of accuracy (certainty) in data, and thematic uncertainty refers to the uncertainty in measures made for each variable, whereas temporal uncertainty refers to the uncertainty in time period represented by each variable. Here, we used climatic data from weather stations to investigate the climate-forcing factors in southern Kenya, and then used this weather station data to investigate the uncertainty in the WorldClim dataset. Results indicated that the nineteen core Worldclim variables, known as bioclimatic variables, accurately depicted the local variations in climate in the study area. However, whereas the monthly and seasonal temperature variables represented the same time period in different locations, the same was not true for the monthly and seasonal precipitation variables. The onset of rains is a key biological indicator, and scientists studying phenomena tied to the onset of rains need to keep in mind the temporal variations represented in the WorldClim dataset.

A Multimedia Web GIS Portal for Promotion of Tourism in Kenya

Tourism is a major foreign exchange earner in Kenya contributing to 10% of the gross domestic product (GDP). Whereas Kenyan government strives to boost its GDP through improved arrivals, lack of effective tourism marketing strategies hinders growth in tourist arrivals in Kenya. To advertise and market the untold wealth of tourist destinations, the government utilizes campaigns through print and electronic media, which are expensive and limited in updating. This study addresses the gap by designing a web Geographic Information System (GIS) portal for marketing and promotion of tourism. To realize this a multimedia GIS database was created using PostgreSQL/PostGIS software to store spatial and multimedia tourism data, while itinerary planning tools were designed using Dijkstra algorithm and Travelling Salesman Problem (TSP) approach. The result was a web GIS portal interface containing tourist information enhanced with text and/or video/audio descriptions. Facebook advertisement was used to popularize the tourism products available in Kenya through visitor engagements as well as directing traffic to the portal fast and inexpensively.

Morphometric and Change Detection Analysis for Prioritization of Sub Basin Conservation, Case Study of Taita Hills

The study is aimed at analyzing the risk of Taita Hills region of harmful runoff and soil erosion by employing morphometric analysis and change detection in a GIS environment to prioritize the Taita Hills in Taita Taveta County. The objective of the study was to characterize and give hierarchy in which the region should be conserved. The methodology adopted hydrological modeling, morphometric computation, Weighted Sum Analysis (WSA) and change detection. Hydrological modeling was vital in delineating the sub-watersheds and stream network. Morphometric computation and WSA was applicable in coming up with parameters and weighting the parameters for each sub-watershed’s prioritization. Change detection is related to how human activity is important for conservation as the effect of land forms and dimensions are compounded. Twenty-one fourth order streamed sub-watersheds were generated and prioritized using morphometry and change detection. Every sub-watershed is given a hierarchy based on the calculated compound parameter from the WSA equation developed and shows the risk of runoff and soil erosion. The morphometric prioritization shows 47% of the watersheds are in the high and very highly susceptible areas and there are two sub-watersheds with the highest land cover change. As well six sub-watersheds are risky with both land cover change and morphometry.

Estimation of Tree Height and Forest Biomass Using Airborne LiDAR Data: A Case Study of Londiani Forest Block in the Mau Complex, Kenya

Tactical decisions on natural resource management require accurate and up to date spatial information for sustainable forest management. Remote sensing devices by the use of multispectral data obtained from satellites or airborne sensors, allow substantial data acquisition that reduce cost of data collection and satisfy demands for continuous precise data. Forest height and Diameter at Breast Height (DBH) are crucial variables to predict volume and biomass. Traditional methods for estimation of tree heights and biomass are time consuming and labour intensive making it difficult for countries to carry out periodic National forest inventories to support forest management and REDD+ activities. This study assessed the applicability of LiDAR data in estimating tree height and biomass in a variety of forest conditions in Londiani Forest Block. The target forests were natural forest, plantation forests and other scattered forests analysed in a variety of topographic conditions. LiDAR data were collected by an aircraft flying at an elevation of 1550 m. The LIDAR pulses hitting the forest were used to estimate the forest height and the density of the vegetation, which implied biomass. LiDAR data were collected in 78 sampling plots of 15 m radius. The LiDAR data were ground truthed to compare its accuracy for above ground biomass (AGB) and height estimation. The correlation coefficients for heights between LiDAR and field data were 0.92 for the pooled data, 0.79 in natural forest, 0.95 in plantation forest and 0.92 in other scattered forest. AGB estimated from LiDAR and ground truthed data had a correlation coefficient of 0.86 for the pooled data, 0.78 in natural forest, 0.84 in plantation forest and 0.51 in other scattered forests. This implied 62%, 84% and 89% accuracy of AGB estimation in natural forests, other scattered forests and plantation forests respectively. The even aged conditions of plantation forests might have resulted to better estimates of height and AGB as compared to uneven aged natural forests and scattered forests. The results imply the reliability of using Airborne LIDAR scanning in forest biomass estimates in Kenya and are an option for supporting a National Forest Monitoring System for REDD+.

Impacts of Mau Forest Catchment on the Great Rift Valley Lakes in Kenya

Remote sensing and GIS applications are being widely used for various projects relating to natural resource management. Forests are very important national assets for economic, environmental protection, social and cultural values and should be conserved in order to realize all these benefits. Kenya’s forests are rapidly declining due to pressure from increased population, technological innovation, urbanization human development and other land uses. Mau forest is one of the major forests in Kenya that is a catchment area for many Great Rift Valley lakes within the country and faces a lot of destruction. Continued destruction of the Mau forest will cause catastrophic environmental damage, resulting in massive food crises and compromising the livelihoods of millions of Kenyans, and the possible collapse of the tourism industry. The purpose of this research was to investigate the relationship between the increasing rate of deforestation and the reduction of the volumes of water in the neighboring lakes between the years 1989 to 2010. Satellite images from Landsat-5 Thematic Mapper (TM) and Landsat-7 Enhanced Thematic Mapper (ETM+) were used for the detection of changes in the Mau forest and the dynamics of the neighboring water bodies that included lakes: Naivasha, Baringo, Nakuru, Elementaita and Bogoria. The research showed that from a period of 1989 to 2010 Mau forest has been decreasing due to deforestation and the water bodies have irregular dynamics in that, from 1989 to 2000, there was rise in the volume of water, this is attributed to the El Nino rains experienced in the country during the year 1997 and 1998. But between 2000 and 2010 the volume decreased as the forest is also decreasing. It is recommended that the government creates awareness to sensitize the public on the importance of such forests as catchment areas in Kenya.

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.

Spatial Modelling of Weather Variables for Plant Disease Applications in Mwea Region

Climate change is expected to affect the agricultural systems, such as crop yield and plant disease occurrence and spread. To be able to mitigate against the negative impacts of climate change, there is a need to use early warning systems that account for expected changes in weather variables such as temperature and rainfall. Moreover, providing such information at high spatial and temporal resolutions can be useful in improving the accuracy of an early warning system. This paper describes a methodology that can be used to produce high spatial and temporal resolutions of minimum temperature, maximum temperature and rainfall in an agricultural area. We utilize MarkSim GCM, a weather file generator that incorporates IPCC based climate change models to downscale the weather variables at monthly intervals. An ensemble of 17 GCM models is used within the RCP 8.0 emission scenario within the latest model based CMIP5. We first assess the usability of the model, by comparing results produced to what has been recorded at weather station level over a vast region. Then, we estimate the correction factors for model results by implementing a linear regression that is used to assess the relationship between the variables and the deviation of model outputs to the weather station data. Finally, we use kriging geostatistical technique to interpolate the weather data, for the year 2010. Results indicated that the model overestimated the results of maximum temperature, while underestimating the result of minimum temperature. Variability in the recorded weather variables was also evident, indicating that the response variables such as plant disease severity dependent on such weather information could vary in the area. These datasets can be useful especially in predicting the occurrence of plant diseases, which are affected by either rainfall or temperature.

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.

Characterization of Forest Degradation beyond Canopy Cover Change in Mau Forest, Kenya

Monitoring Forest degradation is evidence enough to show a country’s commitment to monitor the forest trend both for national and local decision-making and international reporting processes. Unlike deforestation which is easier to point out, monitoring forest degradation is quite a challenge since there is no universal definition and thus no clear monitoring methods apart from the canopy cover change. This research, therefore, sought to look at the degradation trends in the Mau forest complex between 1995-2020 with the aim of finding out whether monitoring canopy density changes over time and quantifying these changes in terms of biomass loss could be a good approach in monitoring forest degradation. Forest Canopy Density (FCD) model was adopted focusing on using vegetation indices describing biophysical conditions of Vegetation, Shadow and Bareness to monitor changes in canopy density as a parameter for describing forest degradation in the forest blocks of Maasai Mau and Olpusimoru in Mau forest complex. Results indicated how different vegetation indices responded to changes in the vegetation density and eventually changes in the canopy density values which were converted in terms of biomass loss. The forest Canopy Density model proved to be a good tool for monitoring forest degradation since it combines different biophysical indices with different characteristics capturing what is happening below the canopy.