This paper explores potential of Remote Sensing and Geospatial Information Systems as viable tools for data collection, processing, transformation and adjustment of cadastral data discrepancies often noted by geospati...This paper explores potential of Remote Sensing and Geospatial Information Systems as viable tools for data collection, processing, transformation and adjustment of cadastral data discrepancies often noted by geospatial practitioners during rasterization and vectorization of land related data. Necessary datasets were collected employing main approach/procedure of scanning, georeferencing, digitization, transformation and analysis in that order, to amalgamate and harmonize all datasets into one common projection and coordinate system （Universal Transverse Mercator （UTM） on Arc-Datum 1960）. Discrepancies in derived areas against recorded values in land registries were noted, smaller parcels exhibited smaller discrepancies and vice versa. Discrepancies were found to be directly proportional to the parcel areas/sizes although large parcels （〉 1000 m2） exhibited abnormally high discrepancies. This procedure yielded systematic discrepancies that could be minimized by use of a fifth order polynomial. Resultant residuals were found to be tolerably low and could be ignored for small parcels （〈 1000 m2）. Final outputs included automated GIS geodatabase cadastre, containing cadastral attributes harmonized to one projection and coordinate system that can be overlaid to other datasets from engineering design and construction works, geological and geotechnical investigation surveys, etc. tied to Remote Sensing data without the requirement of further transformations.展开更多
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 (Senti...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.展开更多
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...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+.展开更多
Location Based Navigation System (LBNS) is a specific Location Based Service (LBS) purely for navigational purpose. These systems resolve position of a user by using GNSS/GPS positioning technologies, to which supplem...Location Based Navigation System (LBNS) is a specific Location Based Service (LBS) purely for navigational purpose. These systems resolve position of a user by using GNSS/GPS positioning technologies, to which supplementary information on goods and services are tagged. The navigation services have become popular and can be installed on mobile phones to provide route information, location of points of interest and user’s current location. LBS has continued to face challenges which include “communication” process towards user reference. Location Based Service System conveys suitable information through a mobile device for effective decision making and reaction within a given time span. This research was geared at understanding the state of LBS technology acceptance and adoption by users in Nairobi Kenya. To do this a quantitative study was carried out through a questionnaire, to investigate mobile phone users’ response on awareness and use of LBS technology. Testing the growth of this technology in this region compared to predictions in previous studies using Technology Acceptance Model (TAM), it is evident that many users may be aware of GPS functionality in mobile phones but are certainly yet to fully embrace the technology as they rarely use it. This points to some underlying challenges towards this technology within this part of the World, thereby recommending for deliberate monitoring and evaluation of LBS technology for sustenance growth based on user satisfaction and acceptance for improved usability.展开更多
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 di...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.展开更多
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 Tav...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.展开更多
Ground deformation measurements are important indicators of subsurface changes which may inform potentially catastrophic events such as structural damage to buildings or dams, derailing of rail lines, and slope failur...Ground deformation measurements are important indicators of subsurface changes which may inform potentially catastrophic events such as structural damage to buildings or dams, derailing of rail lines, and slope failure. Consequently, there is a need for studies to quantify these measurements especially in areas predisposed due to these conditions. One such area is the Menengai caldera in the East African rift where faulting, magmatism and large-scale human activities are happening. This research investigates the magnitude of deformation experienced in Menengai-Subukia area and the relationship with spatial distribution of active faults and human activities such as geothermal development and land use. Sentinel 1 images for the duration 2015 to 2021 were processed in the Sentinel Application Platform (SNAP) using the Terrain Observation with Progressive Scans SAR (TOPSAR) technique. The interferograms showed that subsidence exists within Menengai Geothermal Field (MGF). Structural mapping consisted of automatic lineament extraction in PCI Geomatica using Sentinel 1 images to generate line density map accompanied with Rose diagram which showed concentration and orientation of faults. These faults are attributed to the uplift and subsidence in Menengai caldera due to extension of the stress regime of magma activity below the caldera. Supervised image classification was carried out on Sentinel 2 images in ENVI to generate Land Use Land Cover maps. Validation was done for some reference points (geothermal wells/power plants) and compared with results from the interferograms. This was done by fitting a graphical model of the GPS data and corresponding deformation obtained from the Sentinel 1 interferograms. The findings suggest that the most probable cause of deformation in the area is due to geothermal activities and groundwater abstraction. These techniques, coupled with continuous monitoring could be useful for land-use planning and prediction of geological hazards.展开更多
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 sour...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.展开更多
User response or reaction to navigation applications is influenced by relevance in geographic information, in terms of cartographic context and content delivered within a definite time, providing a direct impact to ou...User response or reaction to navigation applications is influenced by relevance in geographic information, in terms of cartographic context and content delivered within a definite time, providing a direct impact to outcome or consequence based on decision making and hence user reaction. Location Based Navigation Services (LBNS) have continuously advanced in cartographic visualization, making maps interpretation easy and ubiquitous to any user, as compared to pre-historic times when maps were a preserve of a few. Despite rapid growth in LBNS, there exist challenges that may be characterized as technical and non-technical challenges, among them being process of conveying geospatial information to user. LBNS system deliver appropriate information to a user through smartphone (mobile device) for effective decision making and response within a given time span. This research focuses on optimization of cartographic content for contextual information in LBNS to users, based on prevailing circumstances of various components that constitute it. The research looks into Geographic Information Retrieval (GIR), as a technical challenge centered on a non-technical issue of social being of user satisfaction, leading to decision making in LBNS, hence response and outcome. Though advanced technologically, current LBNS on information sourcing depends on user manual web pages navigation and maneuver, this can be painstaking and time consuming that it may cause unnecessary delay in information delivery, resulting to delayed information response time (DIRT). This in turn may lead to unappropriate decision making with erroneous reaction or response being taken, resulting in loss of opportunity, resources, time and even life. Optimization in LBNS is achieved by a mathematical relationship developed between user status, mobile device variables against cartographic content. The relationship is in turn applied in LBNS android application to fulfill optimization solution for user consumption.展开更多
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 ...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.展开更多
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 whic...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.展开更多
Waste generation in Kenya has been increasing with the rapid urbanization(Haregu et al.,2017;Okot-Okumu,2012).Almost 50%of the waste is gener-ated in urban centers,and 0.5 kg per capita waste per day is produced,esti-...Waste generation in Kenya has been increasing with the rapid urbanization(Haregu et al.,2017;Okot-Okumu,2012).Almost 50%of the waste is gener-ated in urban centers,and 0.5 kg per capita waste per day is produced,esti-mated to increase three-fold by 2030.The management of solid waste in Kenya is complicated by various factors.While the national sustainable waste management policy highlights the need for timely inventories and integrated monitoring of waste disposal,there is currently no comprehensive system in place for mapping or monitoring waste disposal sites,both legal and illegal(Ministry of Environment and Forestry,2021).This study aims to identify waste disposal sites in Dandora in 2021 to 2024 through supervised classifi cation,identify MSW spectral interpretation marks from multispectral satel lite imagery and map the spatial distribution of MSW disposal sites using Mobile GIS in Juja in 2022.Supervised classification of the multispectral im agery was performed using training data points from planet imagery,result ing in LULC maps for 2021 to 2024.Spectral reflectance curve charts were generated.Post-classification and location of smaller dumpsites in Juja were collected using mobile GIS.The distribution characteristic of waste disposal sites is associated with densely populated areas of Juja such as areas around Gates A,B and C.Classification results show a high degree of accuracy in identifying and mapping disposal sites across all epochs.In conclusion,high LULC classification accuracy and the other results,indicates that these re-mote sensing techniques,combined with other GIS and field data,can signif icantly enhance waste management in Kenya.展开更多
Lake Victoria,the largest freshwater lake in Africa and a vital resource for over 45 million people across Kenya,Uganda,and Tanzania,is experiencing esca lating environmental degradation due to agricultural runoff,unt...Lake Victoria,the largest freshwater lake in Africa and a vital resource for over 45 million people across Kenya,Uganda,and Tanzania,is experiencing esca lating environmental degradation due to agricultural runoff,untreated sewage,industrial effluents,and solid waste.These pollutants are driving eutrophica-tion,biodiversity loss,and a rise in waterborne diseases,posing serious ecolog-ical and public health threats.This study utilized satellite-based remote sensing and geospatial analytics to assess water quality changes in Lake Victoria over a 20-year period.Cloud-free imagery from the Landsat Collection 2 Tier 1 Sur-face Reflectance dataset-specifically Landsat 7 ETM+(2005),Landsat 5 TM(2010),Landsat 8 OLI/TIRS(2013,2018),and Landsat 9 OLI-2/TIRS-2(2023)-was analyzed using Google Earth Engine to calculate key spectral indices:Nor-malized Difference Vegetation Index(NDWI),Chlorophyll Index(CI),Tur-bidity Index(TI),and Normalized Difference Chlorophyll Index(NDCI).These indices served as proxies for chlorophyll-a,turbidity,and suspended sediment load.Water Quality Index(WQI)values were derived through Python-based scripts,weighted by parameter importance,and classified into four categories:Good,Moderate,Unhealthy,and Very Unhealthy.Results revealed a clear de cline in water quality across the lake,particularly near urban centers such as Kisumu,Bukoba,and Entebbe.Notably,2013 showed an extreme reduction in WQI values,ranging from-8.66 to-330.64,indicating significant pollution levels.The 2023 imagery continued this trend,with WQI values ranging from+69.79 to-130.17,reflecting very high pollution concentrations,especially in eutrophic zones and sediment-laden estuarine regions.The study demonstrates the effectiveness of remote sensing and Python-driven spatial analytics as a scalable,cost-efficient alternative to traditional water monitoring approaches.It recommends institutional adoption of such technologies along with integra tion of satellite data with machine learning models,in-situ measurements,and community-based monitoring frameworks.Ultimately,informing policy,and promoting sustainable,cooperative management of Lake Victoria’s shared wa ter resources.展开更多
文摘This paper explores potential of Remote Sensing and Geospatial Information Systems as viable tools for data collection, processing, transformation and adjustment of cadastral data discrepancies often noted by geospatial practitioners during rasterization and vectorization of land related data. Necessary datasets were collected employing main approach/procedure of scanning, georeferencing, digitization, transformation and analysis in that order, to amalgamate and harmonize all datasets into one common projection and coordinate system （Universal Transverse Mercator （UTM） on Arc-Datum 1960）. Discrepancies in derived areas against recorded values in land registries were noted, smaller parcels exhibited smaller discrepancies and vice versa. Discrepancies were found to be directly proportional to the parcel areas/sizes although large parcels （〉 1000 m2） exhibited abnormally high discrepancies. This procedure yielded systematic discrepancies that could be minimized by use of a fifth order polynomial. Resultant residuals were found to be tolerably low and could be ignored for small parcels （〈 1000 m2）. Final outputs included automated GIS geodatabase cadastre, containing cadastral attributes harmonized to one projection and coordinate system that can be overlaid to other datasets from engineering design and construction works, geological and geotechnical investigation surveys, etc. tied to Remote Sensing data without the requirement of further transformations.
文摘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.
文摘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+.
文摘Location Based Navigation System (LBNS) is a specific Location Based Service (LBS) purely for navigational purpose. These systems resolve position of a user by using GNSS/GPS positioning technologies, to which supplementary information on goods and services are tagged. The navigation services have become popular and can be installed on mobile phones to provide route information, location of points of interest and user’s current location. LBS has continued to face challenges which include “communication” process towards user reference. Location Based Service System conveys suitable information through a mobile device for effective decision making and reaction within a given time span. This research was geared at understanding the state of LBS technology acceptance and adoption by users in Nairobi Kenya. To do this a quantitative study was carried out through a questionnaire, to investigate mobile phone users’ response on awareness and use of LBS technology. Testing the growth of this technology in this region compared to predictions in previous studies using Technology Acceptance Model (TAM), it is evident that many users may be aware of GPS functionality in mobile phones but are certainly yet to fully embrace the technology as they rarely use it. This points to some underlying challenges towards this technology within this part of the World, thereby recommending for deliberate monitoring and evaluation of LBS technology for sustenance growth based on user satisfaction and acceptance for improved usability.
文摘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.
文摘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.
文摘Ground deformation measurements are important indicators of subsurface changes which may inform potentially catastrophic events such as structural damage to buildings or dams, derailing of rail lines, and slope failure. Consequently, there is a need for studies to quantify these measurements especially in areas predisposed due to these conditions. One such area is the Menengai caldera in the East African rift where faulting, magmatism and large-scale human activities are happening. This research investigates the magnitude of deformation experienced in Menengai-Subukia area and the relationship with spatial distribution of active faults and human activities such as geothermal development and land use. Sentinel 1 images for the duration 2015 to 2021 were processed in the Sentinel Application Platform (SNAP) using the Terrain Observation with Progressive Scans SAR (TOPSAR) technique. The interferograms showed that subsidence exists within Menengai Geothermal Field (MGF). Structural mapping consisted of automatic lineament extraction in PCI Geomatica using Sentinel 1 images to generate line density map accompanied with Rose diagram which showed concentration and orientation of faults. These faults are attributed to the uplift and subsidence in Menengai caldera due to extension of the stress regime of magma activity below the caldera. Supervised image classification was carried out on Sentinel 2 images in ENVI to generate Land Use Land Cover maps. Validation was done for some reference points (geothermal wells/power plants) and compared with results from the interferograms. This was done by fitting a graphical model of the GPS data and corresponding deformation obtained from the Sentinel 1 interferograms. The findings suggest that the most probable cause of deformation in the area is due to geothermal activities and groundwater abstraction. These techniques, coupled with continuous monitoring could be useful for land-use planning and prediction of geological hazards.
文摘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.
文摘User response or reaction to navigation applications is influenced by relevance in geographic information, in terms of cartographic context and content delivered within a definite time, providing a direct impact to outcome or consequence based on decision making and hence user reaction. Location Based Navigation Services (LBNS) have continuously advanced in cartographic visualization, making maps interpretation easy and ubiquitous to any user, as compared to pre-historic times when maps were a preserve of a few. Despite rapid growth in LBNS, there exist challenges that may be characterized as technical and non-technical challenges, among them being process of conveying geospatial information to user. LBNS system deliver appropriate information to a user through smartphone (mobile device) for effective decision making and response within a given time span. This research focuses on optimization of cartographic content for contextual information in LBNS to users, based on prevailing circumstances of various components that constitute it. The research looks into Geographic Information Retrieval (GIR), as a technical challenge centered on a non-technical issue of social being of user satisfaction, leading to decision making in LBNS, hence response and outcome. Though advanced technologically, current LBNS on information sourcing depends on user manual web pages navigation and maneuver, this can be painstaking and time consuming that it may cause unnecessary delay in information delivery, resulting to delayed information response time (DIRT). This in turn may lead to unappropriate decision making with erroneous reaction or response being taken, resulting in loss of opportunity, resources, time and even life. Optimization in LBNS is achieved by a mathematical relationship developed between user status, mobile device variables against cartographic content. The relationship is in turn applied in LBNS android application to fulfill optimization solution for user consumption.
文摘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.
文摘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.
文摘Waste generation in Kenya has been increasing with the rapid urbanization(Haregu et al.,2017;Okot-Okumu,2012).Almost 50%of the waste is gener-ated in urban centers,and 0.5 kg per capita waste per day is produced,esti-mated to increase three-fold by 2030.The management of solid waste in Kenya is complicated by various factors.While the national sustainable waste management policy highlights the need for timely inventories and integrated monitoring of waste disposal,there is currently no comprehensive system in place for mapping or monitoring waste disposal sites,both legal and illegal(Ministry of Environment and Forestry,2021).This study aims to identify waste disposal sites in Dandora in 2021 to 2024 through supervised classifi cation,identify MSW spectral interpretation marks from multispectral satel lite imagery and map the spatial distribution of MSW disposal sites using Mobile GIS in Juja in 2022.Supervised classification of the multispectral im agery was performed using training data points from planet imagery,result ing in LULC maps for 2021 to 2024.Spectral reflectance curve charts were generated.Post-classification and location of smaller dumpsites in Juja were collected using mobile GIS.The distribution characteristic of waste disposal sites is associated with densely populated areas of Juja such as areas around Gates A,B and C.Classification results show a high degree of accuracy in identifying and mapping disposal sites across all epochs.In conclusion,high LULC classification accuracy and the other results,indicates that these re-mote sensing techniques,combined with other GIS and field data,can signif icantly enhance waste management in Kenya.
文摘Lake Victoria,the largest freshwater lake in Africa and a vital resource for over 45 million people across Kenya,Uganda,and Tanzania,is experiencing esca lating environmental degradation due to agricultural runoff,untreated sewage,industrial effluents,and solid waste.These pollutants are driving eutrophica-tion,biodiversity loss,and a rise in waterborne diseases,posing serious ecolog-ical and public health threats.This study utilized satellite-based remote sensing and geospatial analytics to assess water quality changes in Lake Victoria over a 20-year period.Cloud-free imagery from the Landsat Collection 2 Tier 1 Sur-face Reflectance dataset-specifically Landsat 7 ETM+(2005),Landsat 5 TM(2010),Landsat 8 OLI/TIRS(2013,2018),and Landsat 9 OLI-2/TIRS-2(2023)-was analyzed using Google Earth Engine to calculate key spectral indices:Nor-malized Difference Vegetation Index(NDWI),Chlorophyll Index(CI),Tur-bidity Index(TI),and Normalized Difference Chlorophyll Index(NDCI).These indices served as proxies for chlorophyll-a,turbidity,and suspended sediment load.Water Quality Index(WQI)values were derived through Python-based scripts,weighted by parameter importance,and classified into four categories:Good,Moderate,Unhealthy,and Very Unhealthy.Results revealed a clear de cline in water quality across the lake,particularly near urban centers such as Kisumu,Bukoba,and Entebbe.Notably,2013 showed an extreme reduction in WQI values,ranging from-8.66 to-330.64,indicating significant pollution levels.The 2023 imagery continued this trend,with WQI values ranging from+69.79 to-130.17,reflecting very high pollution concentrations,especially in eutrophic zones and sediment-laden estuarine regions.The study demonstrates the effectiveness of remote sensing and Python-driven spatial analytics as a scalable,cost-efficient alternative to traditional water monitoring approaches.It recommends institutional adoption of such technologies along with integra tion of satellite data with machine learning models,in-situ measurements,and community-based monitoring frameworks.Ultimately,informing policy,and promoting sustainable,cooperative management of Lake Victoria’s shared wa ter resources.
