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
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 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
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
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 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+.
