Presented by PN Kiunga, PM Kitala, KA Kipronoh, G Mosomtai, J Kiplimo and B Bett at the Regional Conference on Zoonoses in Eastern Africa, Naivasha, Kenya, 9-12 March 2015.
Applications of ecological niche modelling for mapping the risk of Rift Valle...ILRI
Presentation by Purity Kiunga, Philip Kitala, K.A. Kipronoh, Jusper Kiplimo, Gladys Mosomtai and Bernard Bett at the first Sub-Saharan Conference on Spatial and Spatiotemporal Statistics, Johannesburg, South Africa, 17-21 November 2014.
Applications of ecological niche modelling for mapping the risk of Rift Valle...ILRI
Presentation by P.N. Kiunga, P.M. Kitala, K.A. Kipronoh, G. Mosomtai and B. Bett at the 9th biennial scientific conference and exhibition of the Faculty of Veterinary Medicine, University of Nairobi, 3-5 September 2014.
1) UAVSAR collected repeat-pass SAR data over agricultural fields in Canada during a soil moisture campaign to analyze the effect of soil moisture on polarimetric and interferometric measurements.
2) Both backscatter and interferometric phase correlations decreased as soil moisture changed more between passes, indicating soil moisture impacts the SAR measurements.
3) Models captured general backscatter and polarimetric phase trends with soil moisture but underestimated the observed variation in interferometric phase with changes in moisture.
Daily evapotranspiration by combining remote sensing with ground observations...CIMMYT
This document discusses combining remote sensing data with ground observations to estimate daily evapotranspiration (ET) for agricultural water management. It summarizes using remote sensing to model spatial land surface temperature and vegetation cover hourly, integrating them to compute daily ET. It also describes using wireless sensors at an experimental cotton field in Maricopa, Arizona to monitor crops and irrigation as part of an integrated monitoring system for irrigation scheduling. The goal is to provide reasonably accurate and cost-effective daily ET estimates at resolutions useful to growers.
This document presents a method for semi-automated counting of Adélie penguins from aerial photographs of Antarctic colonies. Digital images are analyzed to detect colony areas using color and identify penguins as dark spots. Software allows a user to view detection results, add or remove penguins, and edit counts efficiently while maintaining records. An evaluation with an expert penguin ecologist found the approach helped automate laborious manual counting while keeping fine-scale editing under expert control. Some images remain challenging to classify but overall the semi-automated process was deemed pragmatic for population monitoring.
This document describes research mapping forest height, biomass, and carbon across the contiguous United States from 2000-2007 using satellite and field data. A National Biomass and Carbon Dataset was created in 2000 by combining SRTM radar data, Landsat optical imagery, and field plot measurements. This provided 30m resolution estimates of height, biomass, and carbon. ALOS PALSAR dual-polarization SAR data from 2007 was later used to update biomass estimates. Both datasets were validated against field measurements and showed strong correlations at regional to national scales. The research demonstrated the ability to map important forest variables over large areas by combining different remote sensing and field data sources.
Applications of ecological niche modelling for mapping the risk of Rift Valle...ILRI
Presentation by Purity Kiunga, Philip Kitala, K.A. Kipronoh, Jusper Kiplimo, Gladys Mosomtai and Bernard Bett at the first Sub-Saharan Conference on Spatial and Spatiotemporal Statistics, Johannesburg, South Africa, 17-21 November 2014.
Applications of ecological niche modelling for mapping the risk of Rift Valle...ILRI
Presentation by P.N. Kiunga, P.M. Kitala, K.A. Kipronoh, G. Mosomtai and B. Bett at the 9th biennial scientific conference and exhibition of the Faculty of Veterinary Medicine, University of Nairobi, 3-5 September 2014.
1) UAVSAR collected repeat-pass SAR data over agricultural fields in Canada during a soil moisture campaign to analyze the effect of soil moisture on polarimetric and interferometric measurements.
2) Both backscatter and interferometric phase correlations decreased as soil moisture changed more between passes, indicating soil moisture impacts the SAR measurements.
3) Models captured general backscatter and polarimetric phase trends with soil moisture but underestimated the observed variation in interferometric phase with changes in moisture.
Daily evapotranspiration by combining remote sensing with ground observations...CIMMYT
This document discusses combining remote sensing data with ground observations to estimate daily evapotranspiration (ET) for agricultural water management. It summarizes using remote sensing to model spatial land surface temperature and vegetation cover hourly, integrating them to compute daily ET. It also describes using wireless sensors at an experimental cotton field in Maricopa, Arizona to monitor crops and irrigation as part of an integrated monitoring system for irrigation scheduling. The goal is to provide reasonably accurate and cost-effective daily ET estimates at resolutions useful to growers.
This document presents a method for semi-automated counting of Adélie penguins from aerial photographs of Antarctic colonies. Digital images are analyzed to detect colony areas using color and identify penguins as dark spots. Software allows a user to view detection results, add or remove penguins, and edit counts efficiently while maintaining records. An evaluation with an expert penguin ecologist found the approach helped automate laborious manual counting while keeping fine-scale editing under expert control. Some images remain challenging to classify but overall the semi-automated process was deemed pragmatic for population monitoring.
This document describes research mapping forest height, biomass, and carbon across the contiguous United States from 2000-2007 using satellite and field data. A National Biomass and Carbon Dataset was created in 2000 by combining SRTM radar data, Landsat optical imagery, and field plot measurements. This provided 30m resolution estimates of height, biomass, and carbon. ALOS PALSAR dual-polarization SAR data from 2007 was later used to update biomass estimates. Both datasets were validated against field measurements and showed strong correlations at regional to national scales. The research demonstrated the ability to map important forest variables over large areas by combining different remote sensing and field data sources.
This document discusses spatial and temporal variability observed in radar data of forests. It finds that radar and lidar show similar spatial dynamics at different scales from individual trees to landscapes. Vertical structure variability is observed using SAR tomography, which provides more information than lidar. Temporal change in backscatter and polarimetry is more affected by small look angle changes than time. P-band data shows more stability over time than L-band. InSAR coherence decreases with increasing resolution due to non-compensated spatial variability, but phase and coherence uncertainty is reduced. Good radar product resolution is above 20 meters after multi-looking.
The document discusses validation of SMOS L1c and L2 soil moisture products using airborne and ground-based observations across Australia. It describes environmental conditions and essential climate variables in Australia. It outlines the MoistureMap project which uses data assimilation to provide high-resolution soil moisture information. Field campaigns were conducted in the Murrumbidgee catchment and Arid Zone to collect validation data on soil moisture, vegetation properties, and more to compare to SMOS retrievals from overflying aircraft and satellites. Results showed SMOS L1c brightness temperatures were biased high compared to aircraft measurements but bias was reduced after further processing to L2 soil moisture products.
Michael Hutchinson_Topographic-dependent modelling of surface climate for ear...TERN Australia
This document describes a method for developing high-resolution daily and monthly climate surfaces for Australia using topographic data. The method uses a censored power of normal distribution to parameterize daily rainfall and interpolates anomalies from a background field to incorporate topographic effects. Validation shows the method can downscale climate variables and change scenarios to a 1 km grid with 10-15% accuracy compared to long-term station data. The daily and monthly climate grids can be used to force ecosystem models and assess climate impacts.
This research aims to improve the efficiency of variable rate irrigation (VRI) systems using data collected from sensors on unmanned aircraft. The research uses images from multispectral and thermal sensors on UAS to model evapotranspiration spatially across fields. This evapotranspiration model will be used to prescribe VRI applications to optimize water use. The research presents benefits like reduced pumping costs, nitrate leaching, and yield losses from over/under irrigation. Activities include collecting sensor data on UAS flights, processing images, modeling evapotranspiration, and developing VRI prescriptions. Outcomes include conference presentations and modeling evapotranspiration using UAS sensor data.
Decadal warming slowdown predictions by an artificial neural networkZachary Labe
1. An artificial neural network was developed to predict slowdowns in the rate of decadal global warming by analyzing patterns in ocean heat content anomalies.
2. The neural network found that transitions in the phase of the Interdecadal Pacific Oscillation often preceded warming slowdowns in climate model simulations.
3. Using explainable AI techniques, researchers determined the neural network was leveraging tropical patterns of ocean heat content to make its predictions of when decadal warming rates might slowdown.
The project aims to integrate weather data from NASA missions into NOAA's Pacific Region Integrated Climatology Information Products project. Specifically, the goals are to 1) animate annual tropical storm tracks for 1992, 2002, 2004, 2005 and anomalous high water events near Hawaii, and 2) develop an easy-to-use interface incorporating multiple data layers for each storm event. The area of interest is the Pacific Ocean basin, sometimes focusing on the Cook and Hawaiian Islands. The significance is to contribute geovisualizations to NOAA's decision support tool to reduce coastal vulnerability. Products include interactive geovisualizations and animated annual storm tracks using data from NASA missions like QuikSCAT, TRMM, Jason-1
Japanese encephalitis is a mosquito-borne viral infection of the central nervous system that is endemic in parts of Asia including Nepal. It is a leading cause of viral encephalitis and disability in Asia. The virus is maintained in a cycle between mosquitoes and amplifying hosts like pigs and birds. People in rural agricultural areas are most at risk. Symptoms include fever, headache and seizures, and it can cause long-term neurological effects or death. Prevention strategies include vaccination programs targeting children, mosquito control efforts, and controlling pig populations as they are an amplifying host.
Impact of Emerging Transboundary Diseases, using African Swine Fever in Ugand...SIANI
This study was presented during the conference “Production and Carbon Dynamics in Sustainable Agricultural and Forest Systems in Africa” held in September, 2010.
Rift Valley fever virus: Diagnosis and vaccinesmarketsblog
The document discusses Rift Valley Fever virus, including its diagnosis and vaccines. It provides information on the virus structure and proteins. It then summarizes methods for laboratory diagnosis of Rift Valley Fever during outbreaks and for surveillance. Finally, it reviews currently available and experimental vaccines for Rift Valley Fever, discussing their advantages and disadvantages.
Rift Valley fever is an arthropod-borne viral disease that affects various mammals. It is characterized by abortions in pregnant animals and liver damage. The disease was first described in Kenya in 1931. It is endemic in many African and Middle Eastern countries. Transmission occurs via mosquito bites or contact with infected animal tissues. Symptoms in animals include fever, vomiting, and abortions. The virus can be diagnosed by isolating it from blood or tissues of infected hosts. Controlling mosquito populations and vaccinating susceptible animal species are important for prevention.
Japanese encephalitis is a mosquito-borne viral disease that is common in parts of Asia. It is transmitted to humans via bites from infected Culex mosquitoes. While most infections cause mild symptoms or no symptoms, approximately 1 in 250 infections result in encephalitis, which can be fatal in 30% of cases. Survivors often face permanent neurological impairments. Control efforts focus on vaccination programs and reducing mosquito populations in areas like rice paddies where they breed.
IMED 2018: Predicting the environmental suitability of podoconiosis in EthiopiaLouisa Diggs
This document summarizes a study that aimed to predict the environmental suitability of podoconiosis, a disease causing swelling of the lower limbs, in Ethiopia. The study used cluster sampling to collect data on over 140,000 individuals across Ethiopia. Environmental data on factors like climate, elevation, soil type were extracted for each data point. A machine learning technique called boosted regression trees was used to model the relationship between prevalence of the disease and environmental predictors. The model found disease occurrence increased with altitude, precipitation, silt content and decreased with more alkaline soils. It estimated over 34 million people in Ethiopia live in at risk areas. The study identified regions and environmental factors tied to podoconiosis distribution.
Mapping the distribution of potential Rift Valley fever hotspots in East AfricaILRI
Presentation by Bernard Bett, Jusper Kiplimo, An Notenbaert and Steve Kemp at the 4th Annual East African Community Health and Scientific Conference, Kigali, Rwanda, 27-29 March 2013.
Perspectives of predictive epidemiology and early warning systems for Rift Va...ILRI
Presentation by MO Nanyingi, GM Muchemi, SG Kiama, SM Thumbi and B Bett at the 47th annual scientific conference of the Kenya Veterinary Association held at Mombasa, Kenya, 24-27 April 2013.
Land use change and the risk of selected zoonotic diseases: Observations from...ILRI
Presentation by Bernard Bett, Mohammed Said, Rosemary Sang, Salome Bukachi, Johanna Lindahl, Salome Wanyoike, Ian Njeru and Delia Grace at the 14th conference of the International Society for Veterinary Epidemiology and Economics (ISVEE), Merida, Yucatan, Mexico, 3-7 November 2015.
The document describes a land health surveillance framework to monitor and evaluate land degradation and agroforestry interventions. The framework uses a stratified, randomized sampling approach with sentinel sites, clusters, and plots to collect biophysical data on vegetation, soils, and carbon stocks. Field measurements include woody cover assessments, soil sampling, and allometric methods to estimate biomass and carbon. The goals are to establish baselines, monitor changes over time, target priority areas for interventions, and evaluate outcomes.
The document describes a land health surveillance framework to monitor and evaluate land degradation and agroforestry interventions. The framework uses a stratified, randomized sampling approach with sentinel sites, clusters, and plots to collect biophysical data on vegetation, soils, and carbon stocks. Field measurements include woody cover assessments, soil sampling, and allometric methods to estimate biomass and carbon. The goals are to establish baselines, monitor changes over time, target priority areas for interventions, and evaluate outcomes.
Spatial risk assessment of Rift Valley Fever potential outbreaks using a vect...Nanyingi Mark
Rift Valley fever (RVF) is a vector-borne, viral, zoonotic disease that threatens human and animal health. In Kenya the geographical distribution is determined by spread of competent transmission vectors. Existing RVF predictive risk maps are devoid of vectors interactions with eco-climatic parameters in emergence of disease. We envisage to develop a vector surveillance system (VSS) by mapping the distribution of potential RVF competent vectors in Kenya; To evaluate the correlation between mosquito distribution and environmental-climatic attributes favoring emergence of RVF and investigate by modeling the climatic, ecological and environmental drivers of RVF outbreaks and develop a risk map for spatial prediction of RVF outbreaks in Kenya. Using a cross-sectional design we classified Kenya into 30 spatial units/districts (15 case, 15 control for RVF) based on historical RVF outbreaks weighted probability indices for endemicity. Entomological and ecological surveillance using GPS mapping and monthly (May 2013- February 2014) trapping of mosquitoes is alternatively done in case and control areas. 2500 mosquitoes have been collected in 15 districts (50% geographical target for each for case and control). Species identified as (Culicines-86%, Anophelines-9.7%, Aedes- 2.6%) with over 65% distribution in RVF endemic areas. We demonstrate the applications of spatial epidemiology using GIS to illustrate RVF risk distribution and propose utilizing a Maximum Entropy (MaxEnt) approach to develop Ecological Niche Models (ENM) for prediction of competent RVF vector distributions in un-sampled areas. Targeting RVF hotspots can minimize the costs of large-scale vector surveillance hence enhancing vaccination and vector control strategies. A replicable VSS database and methods can be used for risk analysis of other vector-borne diseases.
Early warning Systems for Vector Borne Climate Sensitive Diseases to Improve...Nanyingi Mark
The document discusses developing an early warning system for vector-borne diseases like malaria and Rift Valley fever in Kenya. It aims to 1) develop tools to detect likely disease outbreaks and 2) assess climate, hydrological, ecosystem and vector factors in high risk areas. The study will analyze disease prevalence, climate/environmental data, and vector surveillance to develop maps overlaying risk factors and disease patterns. This will inform development of predictive models, identify hotspots, and support early warnings to improve human health and resilience against climate-sensitive diseases.
The document outlines Canada's science and applications plan for the NASA Soil Moisture Active Passive (SMAP) mission. Key points include:
1) The plan involves using SMAP soil moisture data for calibration/validation, improving environmental modeling in Canada, and developing applications related to agriculture, drought monitoring, climate, and weather forecasting.
2) Validation sites for soil moisture and freeze/thaw across Canada are described. Field campaigns have been conducted to collect synergistic radar and radiometer data.
3) Research includes developing soil moisture retrieval algorithms, assimilating SMAP data into land surface models, and producing outputs for ecosystem modeling, hydrology and atmospheric modeling.
4) The
Assessment of wheat crop coefficient using remote sensing techniquesPremier Publishers
Irrigation water consumption under physical and climatic conditions for large scale will be easier with remote sensing techniques. Crop evapotranspiration (ETc) uses crop coefficient (Kc) and reference evapotranspiration (ETo). Kc plays an essential role in agricultural practices and it has been widely used to estimate ETc. In this paper Normalized Deference Vegetation Index (NDVI) used to estimate crop coefficient according to satellite data (KcSat) through simple model (KcSat = 2NDVI - 0.2). Landsat8; bands 4 and 5 provide Red (R) and Near Infra-Red (NIR) measurements and it used to calculate NDVI. Single KcFAO estimated under Egyptian conditions according to FAO 56 paper. The KcFAO used to validate KcSat. Linear relationship between KcFAO and KcSat was established and R2 was 0.96. The main objective of this paper is estimation of wheat crop coefficient using remote sensing techniques.
This document discusses spatial and temporal variability observed in radar data of forests. It finds that radar and lidar show similar spatial dynamics at different scales from individual trees to landscapes. Vertical structure variability is observed using SAR tomography, which provides more information than lidar. Temporal change in backscatter and polarimetry is more affected by small look angle changes than time. P-band data shows more stability over time than L-band. InSAR coherence decreases with increasing resolution due to non-compensated spatial variability, but phase and coherence uncertainty is reduced. Good radar product resolution is above 20 meters after multi-looking.
The document discusses validation of SMOS L1c and L2 soil moisture products using airborne and ground-based observations across Australia. It describes environmental conditions and essential climate variables in Australia. It outlines the MoistureMap project which uses data assimilation to provide high-resolution soil moisture information. Field campaigns were conducted in the Murrumbidgee catchment and Arid Zone to collect validation data on soil moisture, vegetation properties, and more to compare to SMOS retrievals from overflying aircraft and satellites. Results showed SMOS L1c brightness temperatures were biased high compared to aircraft measurements but bias was reduced after further processing to L2 soil moisture products.
Michael Hutchinson_Topographic-dependent modelling of surface climate for ear...TERN Australia
This document describes a method for developing high-resolution daily and monthly climate surfaces for Australia using topographic data. The method uses a censored power of normal distribution to parameterize daily rainfall and interpolates anomalies from a background field to incorporate topographic effects. Validation shows the method can downscale climate variables and change scenarios to a 1 km grid with 10-15% accuracy compared to long-term station data. The daily and monthly climate grids can be used to force ecosystem models and assess climate impacts.
This research aims to improve the efficiency of variable rate irrigation (VRI) systems using data collected from sensors on unmanned aircraft. The research uses images from multispectral and thermal sensors on UAS to model evapotranspiration spatially across fields. This evapotranspiration model will be used to prescribe VRI applications to optimize water use. The research presents benefits like reduced pumping costs, nitrate leaching, and yield losses from over/under irrigation. Activities include collecting sensor data on UAS flights, processing images, modeling evapotranspiration, and developing VRI prescriptions. Outcomes include conference presentations and modeling evapotranspiration using UAS sensor data.
Decadal warming slowdown predictions by an artificial neural networkZachary Labe
1. An artificial neural network was developed to predict slowdowns in the rate of decadal global warming by analyzing patterns in ocean heat content anomalies.
2. The neural network found that transitions in the phase of the Interdecadal Pacific Oscillation often preceded warming slowdowns in climate model simulations.
3. Using explainable AI techniques, researchers determined the neural network was leveraging tropical patterns of ocean heat content to make its predictions of when decadal warming rates might slowdown.
The project aims to integrate weather data from NASA missions into NOAA's Pacific Region Integrated Climatology Information Products project. Specifically, the goals are to 1) animate annual tropical storm tracks for 1992, 2002, 2004, 2005 and anomalous high water events near Hawaii, and 2) develop an easy-to-use interface incorporating multiple data layers for each storm event. The area of interest is the Pacific Ocean basin, sometimes focusing on the Cook and Hawaiian Islands. The significance is to contribute geovisualizations to NOAA's decision support tool to reduce coastal vulnerability. Products include interactive geovisualizations and animated annual storm tracks using data from NASA missions like QuikSCAT, TRMM, Jason-1
Japanese encephalitis is a mosquito-borne viral infection of the central nervous system that is endemic in parts of Asia including Nepal. It is a leading cause of viral encephalitis and disability in Asia. The virus is maintained in a cycle between mosquitoes and amplifying hosts like pigs and birds. People in rural agricultural areas are most at risk. Symptoms include fever, headache and seizures, and it can cause long-term neurological effects or death. Prevention strategies include vaccination programs targeting children, mosquito control efforts, and controlling pig populations as they are an amplifying host.
Impact of Emerging Transboundary Diseases, using African Swine Fever in Ugand...SIANI
This study was presented during the conference “Production and Carbon Dynamics in Sustainable Agricultural and Forest Systems in Africa” held in September, 2010.
Rift Valley fever virus: Diagnosis and vaccinesmarketsblog
The document discusses Rift Valley Fever virus, including its diagnosis and vaccines. It provides information on the virus structure and proteins. It then summarizes methods for laboratory diagnosis of Rift Valley Fever during outbreaks and for surveillance. Finally, it reviews currently available and experimental vaccines for Rift Valley Fever, discussing their advantages and disadvantages.
Rift Valley fever is an arthropod-borne viral disease that affects various mammals. It is characterized by abortions in pregnant animals and liver damage. The disease was first described in Kenya in 1931. It is endemic in many African and Middle Eastern countries. Transmission occurs via mosquito bites or contact with infected animal tissues. Symptoms in animals include fever, vomiting, and abortions. The virus can be diagnosed by isolating it from blood or tissues of infected hosts. Controlling mosquito populations and vaccinating susceptible animal species are important for prevention.
Japanese encephalitis is a mosquito-borne viral disease that is common in parts of Asia. It is transmitted to humans via bites from infected Culex mosquitoes. While most infections cause mild symptoms or no symptoms, approximately 1 in 250 infections result in encephalitis, which can be fatal in 30% of cases. Survivors often face permanent neurological impairments. Control efforts focus on vaccination programs and reducing mosquito populations in areas like rice paddies where they breed.
IMED 2018: Predicting the environmental suitability of podoconiosis in EthiopiaLouisa Diggs
This document summarizes a study that aimed to predict the environmental suitability of podoconiosis, a disease causing swelling of the lower limbs, in Ethiopia. The study used cluster sampling to collect data on over 140,000 individuals across Ethiopia. Environmental data on factors like climate, elevation, soil type were extracted for each data point. A machine learning technique called boosted regression trees was used to model the relationship between prevalence of the disease and environmental predictors. The model found disease occurrence increased with altitude, precipitation, silt content and decreased with more alkaline soils. It estimated over 34 million people in Ethiopia live in at risk areas. The study identified regions and environmental factors tied to podoconiosis distribution.
Mapping the distribution of potential Rift Valley fever hotspots in East AfricaILRI
Presentation by Bernard Bett, Jusper Kiplimo, An Notenbaert and Steve Kemp at the 4th Annual East African Community Health and Scientific Conference, Kigali, Rwanda, 27-29 March 2013.
Perspectives of predictive epidemiology and early warning systems for Rift Va...ILRI
Presentation by MO Nanyingi, GM Muchemi, SG Kiama, SM Thumbi and B Bett at the 47th annual scientific conference of the Kenya Veterinary Association held at Mombasa, Kenya, 24-27 April 2013.
Land use change and the risk of selected zoonotic diseases: Observations from...ILRI
Presentation by Bernard Bett, Mohammed Said, Rosemary Sang, Salome Bukachi, Johanna Lindahl, Salome Wanyoike, Ian Njeru and Delia Grace at the 14th conference of the International Society for Veterinary Epidemiology and Economics (ISVEE), Merida, Yucatan, Mexico, 3-7 November 2015.
The document describes a land health surveillance framework to monitor and evaluate land degradation and agroforestry interventions. The framework uses a stratified, randomized sampling approach with sentinel sites, clusters, and plots to collect biophysical data on vegetation, soils, and carbon stocks. Field measurements include woody cover assessments, soil sampling, and allometric methods to estimate biomass and carbon. The goals are to establish baselines, monitor changes over time, target priority areas for interventions, and evaluate outcomes.
The document describes a land health surveillance framework to monitor and evaluate land degradation and agroforestry interventions. The framework uses a stratified, randomized sampling approach with sentinel sites, clusters, and plots to collect biophysical data on vegetation, soils, and carbon stocks. Field measurements include woody cover assessments, soil sampling, and allometric methods to estimate biomass and carbon. The goals are to establish baselines, monitor changes over time, target priority areas for interventions, and evaluate outcomes.
Spatial risk assessment of Rift Valley Fever potential outbreaks using a vect...Nanyingi Mark
Rift Valley fever (RVF) is a vector-borne, viral, zoonotic disease that threatens human and animal health. In Kenya the geographical distribution is determined by spread of competent transmission vectors. Existing RVF predictive risk maps are devoid of vectors interactions with eco-climatic parameters in emergence of disease. We envisage to develop a vector surveillance system (VSS) by mapping the distribution of potential RVF competent vectors in Kenya; To evaluate the correlation between mosquito distribution and environmental-climatic attributes favoring emergence of RVF and investigate by modeling the climatic, ecological and environmental drivers of RVF outbreaks and develop a risk map for spatial prediction of RVF outbreaks in Kenya. Using a cross-sectional design we classified Kenya into 30 spatial units/districts (15 case, 15 control for RVF) based on historical RVF outbreaks weighted probability indices for endemicity. Entomological and ecological surveillance using GPS mapping and monthly (May 2013- February 2014) trapping of mosquitoes is alternatively done in case and control areas. 2500 mosquitoes have been collected in 15 districts (50% geographical target for each for case and control). Species identified as (Culicines-86%, Anophelines-9.7%, Aedes- 2.6%) with over 65% distribution in RVF endemic areas. We demonstrate the applications of spatial epidemiology using GIS to illustrate RVF risk distribution and propose utilizing a Maximum Entropy (MaxEnt) approach to develop Ecological Niche Models (ENM) for prediction of competent RVF vector distributions in un-sampled areas. Targeting RVF hotspots can minimize the costs of large-scale vector surveillance hence enhancing vaccination and vector control strategies. A replicable VSS database and methods can be used for risk analysis of other vector-borne diseases.
Early warning Systems for Vector Borne Climate Sensitive Diseases to Improve...Nanyingi Mark
The document discusses developing an early warning system for vector-borne diseases like malaria and Rift Valley fever in Kenya. It aims to 1) develop tools to detect likely disease outbreaks and 2) assess climate, hydrological, ecosystem and vector factors in high risk areas. The study will analyze disease prevalence, climate/environmental data, and vector surveillance to develop maps overlaying risk factors and disease patterns. This will inform development of predictive models, identify hotspots, and support early warnings to improve human health and resilience against climate-sensitive diseases.
The document outlines Canada's science and applications plan for the NASA Soil Moisture Active Passive (SMAP) mission. Key points include:
1) The plan involves using SMAP soil moisture data for calibration/validation, improving environmental modeling in Canada, and developing applications related to agriculture, drought monitoring, climate, and weather forecasting.
2) Validation sites for soil moisture and freeze/thaw across Canada are described. Field campaigns have been conducted to collect synergistic radar and radiometer data.
3) Research includes developing soil moisture retrieval algorithms, assimilating SMAP data into land surface models, and producing outputs for ecosystem modeling, hydrology and atmospheric modeling.
4) The
Assessment of wheat crop coefficient using remote sensing techniquesPremier Publishers
Irrigation water consumption under physical and climatic conditions for large scale will be easier with remote sensing techniques. Crop evapotranspiration (ETc) uses crop coefficient (Kc) and reference evapotranspiration (ETo). Kc plays an essential role in agricultural practices and it has been widely used to estimate ETc. In this paper Normalized Deference Vegetation Index (NDVI) used to estimate crop coefficient according to satellite data (KcSat) through simple model (KcSat = 2NDVI - 0.2). Landsat8; bands 4 and 5 provide Red (R) and Near Infra-Red (NIR) measurements and it used to calculate NDVI. Single KcFAO estimated under Egyptian conditions according to FAO 56 paper. The KcFAO used to validate KcSat. Linear relationship between KcFAO and KcSat was established and R2 was 0.96. The main objective of this paper is estimation of wheat crop coefficient using remote sensing techniques.
We present a survey of computational and applied mathematical techniques that have the potential to contribute to the next generation of high-fidelity, multi-scale climate simulations. Examples of the climate science problems that can be investigated with more depth with these computational improvements include the capture of remote forcings of localized hydrological extreme events, an accurate representation of cloud features over a range of spatial and temporal scales, and parallel, large ensembles of simulations to more effectively explore model sensitivities and uncertainties.
Numerical techniques, such as adaptive mesh refinement, implicit time integration, and separate treatment of fast physical time scales are enabling improved accuracy and fidelity in simulation of dynamics and allowing more complete representations of climate features at the global scale. At the same time, partnerships with computer science teams have focused on taking advantage of evolving computer architectures such as many-core processors and GPUs. As a result, approaches which were previously considered prohibitively costly have become both more efficient and scalable. In combination, progress in these three critical areas is poised to transform climate modeling in the coming decades.
Evaluating Satellite Precipitation Error Propagation in Runoff Simulations of...Yiwen Mei
This study investigates the error characteristics of six quasi-global satellite precipitation products and associated error propagation in flow simulations for 16 mountainous basin scales (areas ranging from 255 to 6967 km2) and two different periods (May-Aug & Sep-Nov) in northeast Italy. The satellite products used in this study are 3B42-CCA, 3B42-V7, CMORPH and PERSIANN with their respect gauge-adjusted products. To evaluate the error propagation in flood simulations satellite precipitation datasets were used to force a gauge-calibrated hydrologic model to simulate runoff for the 16 basins, and comparing them to the gauge-driven simulated hydrographs for a range of moderate to high flood events spanning a nine-year period (2002 to 2009). Statistics describing the systematic and random error, the temporal similarity and error ratios between precipitation and runoff are presented.
Dr Jerome O Connell - presentation made at various conferences throughout Europe as part of PhD which was funded by the EPA under the STRIVE Research Programme 2007-2013 (2007-PhD-ET-2)
Density and distribution of chimpanzee (Pan troglodytes verus, Schwarz 1934) ...Open Access Research Paper
The loss of biodiversity mainly due to human activities is a global concern. The survival of wild mammals, including the West African chimpanzee (Pan troglodytes verus), which is considered a critically endangered species, is threatened. However, information on the status of the remaining populations of such a primate and its distribution is rarely available or out of date for some sites. This study aims at improving the knowledge of the west chimpanzee population density and distribution in Mont Sangbé National Park (MSNP), West Côte d’Ivoire, for conservation purposes. We counted chimpanzee sleeping nests along 64 line transects of one kilometer each in the forest area of the MSNP by following distance sampling methods. Then, we recorded the GPS coordinates of all signs of the presence of the species during transects and recce surveys. We observed 148 signs of the presence of chimpanzees including 94 nests counted along transects. The average density of chimpanzees in the forest area of MSNP was estimated at 0.25 individuals/km² and 0.48 individuals/km² when using a value of a lifetime of nests of 164.38 days and 84.38 days, respectively. In addition, the distribution map showed that the signs of the presence of chimpanzees are mainly observed in two areas: the southern and the north-eastern forest areas of the MSNP. We recommend the application of other survey methods (genetics, camera trapping, nest counts combined with the modeling of nest lifetime estimates) for a better understanding of the chimpanzee population ecology and for conservation management in the PNMS.
Kasper Johansen_Field and airborne data collection by AusCover: a tropical ra...TERN Australia
The document describes a field and airborne data collection campaign conducted by AusCover in a tropical rainforest site called Robson Creek. The campaign involved collecting field data through measurements of vegetation structure, leaf area index, hemispherical photos, and more. Airborne data was also collected through LiDAR and hyperspectral imaging from aircraft. The data collection was designed to validate satellite data and the various data sets would be made available through an online portal and shared with collaborators for research on topics like biomass estimation and vegetation mapping.
This document discusses using geographic information systems (GIS) and remote sensing to study and conserve two endangered ungulate species in Mongolia, the Asiatic wild ass or khulan and the goitered gazelle. GPS collars were used to track the movements of 20 khulans and 10 gazelles, finding that they range widely, including important habitat areas outside of protected areas. Ground surveys estimated populations of around 36,000 khulans and 28,000 gazelles in Southern Gobi. Spatial modeling identified surface water and human disturbance as most influencing species distributions, with around 25% of suitable habitat for each located within protected areas. GIS and remote sensing were useful conservation planning tools.
Andy Lowe_Space as a proxy for time: the Australian Transect NetworkTERN Australia
The Australian Transect Network establishes long-term monitoring sites along environmental gradients across Australia to track changes in species composition, traits, and genes over time and space in response to climate change, using space as a proxy for time. Data from the transects are used to validate models, engage citizens in science, and inform policy decisions about landscape management and climate adaptation. The network includes transects in northern, southwestern, and southeastern Australia that monitor vegetation and wildlife along climatic and disturbance gradients.
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Using ecological niche modelling for mapping the risk of Rift Valley fever in Kenya
1. Using ecological niche modelling for mapping
the risk of Rift Valley fever in Kenya
Purity N. Kiunga(UoN/ILRI); Philip M. Kitala (UoN); K.A. Kipronoh (KARI);
Gladys Mosomtai (ICIPE); Jusper Kiplimo (ILRI) and Bernard Bett (ILRI)
Regional Conference on Zoonotic Diseases in Eastern Africa
Naivasha, Kenya
9-12 March 2015
The Sch
4. Background
Rift Valley fever (RVF) is an acute febrile arthropod-
borne zoonotic disease
Aetiology: RVFV, family Bunyaviridae, genus
Phlebovirus
RVF history in Kenya
1912: First report of RVF-like disease in sheep
1931: Virus isolation and confirmation (Daurbney et al.
1931)
2006/2007: Last outbreak in Kenya
5. Background
• RVF NICHE
El Niño/Southern Oscillation (ENSO) –causing flooding
soil types- solonetz, solanchaks, planosols
Elevation-less than 1100m asl
Natural Difference Vegetation Index (NDVI)- 0.1 units
more than 3 months
Vector- Aedes ,Culicine and others
Temperature
(Linthicum et al. 1999; Anyamba et al. 2009; Hightower
et al. 2012; Bett et al. 2013)
6. Objective
Map RVF potential distribution
Disease occurrence maps
This study used Ecological Niche Modelling:
•Uses presence data
• Shows potential areas where RVF can occur
8. Methodology
>ENM
Strategy for estimating the actual or potential
geographic distribution of a species; is to characterize
the environmental conditions that are suitable for the
species and then identify where suitable environments
are distributed in space
l
10. ENM
Environmental layers
– Land use and land cover maps
– Precipitation
– NDVI
– Temperature
– Elevation
– Soil types
Occurrence data
-Data describing the known distribution of a species (RVF) exist
in a GIS format – point data (lat, long)
11. ENM
Algorithms(GARP)
Genetic Algorithm for Rule set Production (GARP); an open
modeller software creates ecological niche models for species
GARP algorithm was used to map the actual and potential
distribution of Rift Valley fever distribution in Kenya and result
compared to Random Forest Cover
Uses rules of selection, evaluation, testing and incorporation or
rejection in modelling
12. ENM
Evaluation
Assess the accuracy (Confusion matrix)
•Area Under Cover (AUC)
>Defined by plotting sensitivity against 1 specificity across the
range of possible thresholds of 0.82
(Swets 1988 and Manel et al. 2001; AUC of 0.5 – 0.7=poor, 0.7 –
0.9=moderate and >0.9 is high performance)
•A Partial receiver operating characteristic (ROC) analyses AUC
prediction with a value of 1.77 (0= not good, at 1.0=very good and
2.0=excellent)
13. ENM
•ENM output was compared with Random forest
(covers more spatial areas and shows consistency)
•Jackknife analysis=Variable analysis
14. LOGIT
•2 years data (2006-07)
•Case-control design cases(grid 25 by 25km +ve (20%)
control)
•Done to rank variables contributing to output
•Input (soil, rain, NDVI, elevation, temperature)
17. Jackknife output rainfall and temperature
0 10 20 30 40 50 60 70 80 90 100
OCT
NOV
DEC
JAN
FEB
MAR
RAINFALL
TEMPERATURE
18. Discussion
•ENM only shows spatial distribution areas of the
disease but doesn’t show variable contribution to
output (correlation)
•Show potential and high risk areas where disease
can occur
•Both models important shows consistency
•Logit done= ranks variable contribution to output
and shows relationship between variables
19. Conclusion
This will help policymakers to know
which areas to focus their attention
and put plans in place when the
outbreak occurs again
21. The presentation has a Creative Commons licence. You are free to re-use or distribute this work, provided credit is given to ILRI.
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