No description available.

I studied movement characteristics and vegetative resources effects on home range size of beavers at Redstone Arsenal (RSA) in north central Alabama, USA. Beavers were captured and radio tagged from 11 wetlands during winter and spring of 2011. I monitored movements of radio-tagged beavers using radio telemetry from May 2011–April 2012. Beavers moved faster, presumably more favorable to central place foraging, in wetland as they proceeded farther away from the central place, but did not in upland. Additionally, distributions of hourly distances from lodges were bimodal. Home range, core areas, and distance from lodge did not differ between age classes. Home range sizes increased with increasing habitat productivity and resource dispersion, whereas home ranges decreased with temporal variation in resources throughout the year. Quantity and spatial distribution of resources and patterns of foraging behavior influence movements and home ranges of central place foragers.

The problem of whether Landsat 7 data could be used to delineate areas prone to mass movement, particularly debris flows and landslides, was examined using three techniques: change detection in NDVI (Normalized Difference Vegetation Index), change detection in band 5, and the tasseled cap transformation. These techniques were applied to areas that had recently experienced mass movement: Layton, Davis County and Alpine, Spanish Fork Canyon and Santaquin, Utah County. No distinctive spectral characteristics were found with any of these techniques with two possible explanations: 1. That despite improved spatial resolution in Landat 7 over its predecessors and improved digital image processing capabilities, the resolution is still too low to detect these characteristics or 2. That the aspects of a slope that make it prone to mass movement are undetectable at any resolution by remote sensing. Change detection in NDVI examined if areas that remained unchanged (defined as < 5% change) between August 14, 1999 and October 17, 1999 correlated to areas that are prone to mass movement. There was no correlation. Change detection in band 5 was examined between August 14, 1999 and October 17, 1999, October 17, 1999 and May 28, 2000, and August 14, 1999 and May 28, 2000. An interesting result is that the Shurtz Lake and Thistle landslides (Spanish Fork Canyon) showed changes of greater than 30% during August 14, 1999 - October 17, 1999 and October 17, 1999 - May 28, 2000. These changes were limited to these landslides and not seen in abundance in surrounding areas. A similar localization of 30% change was seen in the Cedar Bench landslide (Layton) for the same time periods. There were no other correlations. The tasseled cap ransformation shows areas of dominate greenness, soil brightness or wetness. None of these factors had distinctive patterns in the areas studied when compared to surrounding, mass movement-prone areas so no conclusions can be drawn about the utility of the tasseled cap transformation as it relates to areas of potential mass movement.

No description available.

The need for water conservation continues to increase as global freshwater resources dwindle. In response, many golf course superintendents are implementing new methods and tools to become more frugal with their water applications. For example, scheduling irrigation using time-domain reflectometer (TDR) soil moisture sensors can decrease water usage. Still, TDR measurements are time-consuming and only cover small scales, leading to many locations being unsampled. Remotely sensed data such as the normalized difference vegetation index (NDVI) offer the potential of estimating moisture stress across larger scales; however, NDVI measurements are influenced by numerous stressors beyond moisture availability, thus limiting its reliability for irrigation decisions. An alternative vegetation index, the water band index (WBI), is primarily influenced by water absorption within a narrow spectral range of near-infrared light. Previous research has established strong relationships between moisture stress of creeping bentgrass (CBG) grown on sand-based root zones, a typical scenario for golf course putting greens. However, this relationship characterizes only a small portion of total acreage across golf courses, which limits widespread adoption. In our research, '007' CBG and 'Latitude 36'hybrid bermudagrass (HBG) were grown on three soil textures, USGA 90:10 sand (S), sand loam (SL) and clay (C), arranged in a 2 x 3 factorial design, randomized within six individual dry-down cycles serving as replications. Canopy reflectance and volumetric water content (VWC) data were collected hourly between 0700 and 1900 hr using a hyperspectral radiometer and an embedded soil moisture sensor, until complete turf necrosis. The WBI had the strongest relationship to VWC (r = 0.62) and visual estimations of wilt (r = -0.91) compared to the green-to-red ratio index (GRI) or NDVI. Parameters associated with non-linear regression were analyzed to compare grasses, soils, indices, and their interactions. The WBI and GRI compared favorably with each other and indicated significant moisture stress approximately 28 hr earlier than NDVI (P = 0.0010). WBI and GRI respectively predicted moisture stress 12 to 9 hr before visual estimation of 50% wilt, whereas NDVI provided 2 hr of prediction time (P = 0.0317). When considering the time to significant moisture stress, the HBG lasted 28 hr longer than CBG, while S lasted 42 hr longer than either SL and C (P ≤ 0.0011). Nonlinear regression analysis showed that WBI and GRI can be useful for predicting moisture stress of CBG and HBG grown on three diverse soils in a highly controlled environment. Our results provide substantial evidence and direction for future research investigating how WBI and GRI can expedite moisture stress assessment and prediction on a large-acreage basis. / Master of Science in Life Sciences / Managed turfgrasses provide several benefits including filtering pollutants, cooling their surroundings, generating oxygen, preventing erosion, serving as recreational surfaces, and increasing landscape aesthetics. Intensively managed turfgrass systems, such as on golf courses and sports fields, require more inputs to maintain acceptable conditions. Freshwater use is often excessive on intensively managed turfgrasses to maintain proper plant growth. Drought conditions often limit water availability, especially in regions with limited rainfall. Turf managers tend to over-apply water across large acreage when few localized areas begin to show symptoms of drought. Additionally, turf managers sometimes wrongly identify stressed areas from other factors as ones being moisture-deprived. Advancements such as the use of soil moisture meters have simplified irrigation decisions as an aid to visual inspections for drought stress. While this method enhances detection accuracy, it still provides no solution to increase efficiency. Expanding our current knowledge of turfgrass canopy light reflectance for rapid moisture stress identification can potentially save both time and water resources. The objective of this research was to enhance our ability to identify and predict moisture stress of creeping bentgrass (CBG) and hybrid bermudagrass (HBG) canopies integrated into varying soil textures (USGA 90:10 sand (S), sand loam (SL) and Clay (C)) using light reflectance measurements. Dry-down cycles were conducted under greenhouses conditions collecting soil moisture and light reflectance data every hour from 7 am to 7 pm after saturating and withholding water from established plugs. Moisture stress was most accurately estimated over time using two vegetation indices, the water band index (WBI) and green-to-red ratio index (GRI), with approximately ninety percent accuracy to visible wilt stress. The WBI and GRI predicted moisture stress of CBG in all soil types and HBG in SL and C approximately 14 hours before the grasses reached 50% wilt. While light reflectance varies on exposed soils, our research shows that underlying soils do not interfere with measurements across typical turfgrass stands. This research provides a foundation for future research implementing rapid, aerial measurements of moisture stressed turfgrasses on a broad application of CBG and HBG on constructed or native soils.

Policy makers adopt irrigated agriculture for food security, since irrigation doubles crop production. Therefore, the development of large irrigation systems has a long history in many places worldwide. Although large-scale irrigation schemes play an important role in improving food security, many schemes, especially in Africa, do not yield the expected outcomes. This is related to poor water management, which is generally due to a lack of effective evaluation and monitoring. The objective of this study, therefore, is to propose a new methodology to assess, evaluate and monitor large-scale irrigation systems. Information on irrigation indicators is needed to enable the evaluation of irrigation performance. The evaluation is the first and the most significant step in providing information about how it is performing. After reviewing extensive literature, a list of indicators related to the performance of irrigation, rainwater supply and productivity is suggested. The irrigation efficiency indicators Relative Irrigation Supply (RIS) and Relative Water Supply (RWS) are selected. Potential rainwater supply to crops can be tested based on the Moisture Availability Index (MAI) and the Ratio of Moisture Availability (RMA). Water productivity can be assessed by Crop Yield (Y) and Water Use Efficiency (WUE). However, the central problem facing large-scale irrigation schemes is always the lack of data, which calls for the development of a new method of data acquisition that allows evaluation and monitoring. Remote Sensing (RS) technology makes it possible to retrieve data across large areas. Two different approaches via RS, the Normalized Difference Vegetation Index (NDVI) and Actual Evapotranspiration (ETa), can be utilized for monitoring. The well-known Vegetation Condition Index (VCI), derived from the NDVI, is modified (MVCI) to allow a qualitative spatio-temporal assessment of irrigation efficiency. MVCI takes into account crop response to water availability, while ETa indicates whether water is used as intended. Furthermore, the assessment of the possible hydrological impact of the irrigation system should be considered in the evaluation and monitoring process. The Sudanese Gezira Scheme of 8,000 square kilometers in the Nile Basin, where performance evaluation and monitoring are absent or poorly conducted, is no exception. This research takes the large-scale irrigation of the Gezira Scheme as a case study, as it is the largest scheme, not only in the Nile Basin but also in the world, under single management. The first long-term historical evaluation of the scheme is conducted for the period 1961–2012 rather than only on a short-time scale as is the common practice. An increase in RIS and RWS values from 1.40 and 1.70 to 2.23 and 2.60, respectively, since the 1993/94 season shows decreasing irrigation efficiency. MAI and RMA for summer crops indicate a promising rainfall contribution to irrigation in July and August. The Gezira Scheme achieves low yield and WUE in comparison to many irrigation schemes of the globe. Low productivity is mainly due to poor distribution and irrigation mismanagement. This is indicated by the 15-year MVCI spatio-temporal analysis, which shows that the northern part of the scheme experiences characteristic drought during the summer crop season. Although MVCI can be considered a monitoring tool, the index does not deduct the soil water content, and water could be wasted and available in other ways (e.g. water depressions). Spatio-temporal information for ETa is required to better quantify water depletion and establish links between land use and water allocation. However, several RS models have been developed for estimating ETa. Thus, improving the understanding of performance of such models in arid climates, as well as large-scale irrigation schemes, is taken into account in this study. Four different models based on the energy balance method, the Surface Energy Balance Algorithm for Land (SEBAL), Mapping EvapoTranspiration at High Resolution with Internalized Calibration (METRIC™), Simplified Surface Energy Balance (SSEB) and MOD16 ET are applied in order to determine the optimal approach for obtaining ETa. Outputs from these models are compared to actual water balance (WB) estimates during the 2004/05 season at field scale. Several statistical measures are evaluated, and a score is given for each model in order to select the best-performing model. Based on ranking criteria, SSEB gives the best performance and is seen as a suitable operational ETa model for the scheme. SSEB subsequently is applied for summer and winter crop seasons for the period 2000–2014. Unfortunately, one of the limitations faced in the current research is the absence of validation data on a regional scale. Therefore, the assessment focuses on spatial distribution and trends rather than absolute values. As with the MVCI distribution, the seasonal ETa for the Gezira Scheme is higher in the southern and central parts than in the northern part. This confirms the robustness of the developed MVCI. To avoid using absolute values of ETa, the ratio of ETa from agricultural areas (ETagr) to the total evapotranspiration (ET) from the scheme (ETsum) is calculated. The ETagr/ETsum ratio shows a descending trend over recent years, indicating that the water is available but not being utilized for agricultural production. This study shows that SSEB is also useful for identifying the location of water losses on a daily basis. Around 80 channels are identified as having leakage problems for the 2013/14 crop season. Such information is very useful for reducing losses at the scheme. In addition, Rainwater Harvesting (WH) is addressed and found to be applicable as an alternative solution for accounting for rainfall in irrigation. It is seen that these management scenarios could save water and increase the overall efficiency of the scheme. It is possible to save 68 million cubic meters of water per year when the overall irrigation efficiency of the scheme is improved by only 1%. A level of efficiency of 75% is predicted from the proposed management scenarios, which could save about 2.6 billion cubic meters of water per year. In conclusion, the present study has developed an innovative method of identifying the problems of large-scale schemes as well as proposing management scenarios to enhance irrigation water management practice. Improved agricultural water management in terms of crop, water and land management can increase food production, thereby alleviating poverty and hunger in an environmentally sustainable manner.

Policy makers adopt irrigated agriculture for food security, since irrigation doubles crop production. Therefore, the development of large irrigation systems has a long history in many places worldwide. Although large-scale irrigation schemes play an important role in improving food security, many schemes, especially in Africa, do not yield the expected outcomes. This is related to poor water management, which is generally due to a lack of effective evaluation and monitoring. The objective of this study, therefore, is to propose a new methodology to assess, evaluate and monitor large-scale irrigation systems. Information on irrigation indicators is needed to enable the evaluation of irrigation performance. The evaluation is the first and the most significant step in providing information about how it is performing. After reviewing extensive literature, a list of indicators related to the performance of irrigation, rainwater supply and productivity is suggested. The irrigation efficiency indicators Relative Irrigation Supply (RIS) and Relative Water Supply (RWS) are selected. Potential rainwater supply to crops can be tested based on the Moisture Availability Index (MAI) and the Ratio of Moisture Availability (RMA). Water productivity can be assessed by Crop Yield (Y) and Water Use Efficiency (WUE). However, the central problem facing large-scale irrigation schemes is always the lack of data, which calls for the development of a new method of data acquisition that allows evaluation and monitoring. Remote Sensing (RS) technology makes it possible to retrieve data across large areas. Two different approaches via RS, the Normalized Difference Vegetation Index (NDVI) and Actual Evapotranspiration (ETa), can be utilized for monitoring. The well-known Vegetation Condition Index (VCI), derived from the NDVI, is modified (MVCI) to allow a qualitative spatio-temporal assessment of irrigation efficiency. MVCI takes into account crop response to water availability, while ETa indicates whether water is used as intended. Furthermore, the assessment of the possible hydrological impact of the irrigation system should be considered in the evaluation and monitoring process. The Sudanese Gezira Scheme of 8,000 square kilometers in the Nile Basin, where performance evaluation and monitoring are absent or poorly conducted, is no exception. This research takes the large-scale irrigation of the Gezira Scheme as a case study, as it is the largest scheme, not only in the Nile Basin but also in the world, under single management. The first long-term historical evaluation of the scheme is conducted for the period 1961–2012 rather than only on a short-time scale as is the common practice. An increase in RIS and RWS values from 1.40 and 1.70 to 2.23 and 2.60, respectively, since the 1993/94 season shows decreasing irrigation efficiency. MAI and RMA for summer crops indicate a promising rainfall contribution to irrigation in July and August. The Gezira Scheme achieves low yield and WUE in comparison to many irrigation schemes of the globe. Low productivity is mainly due to poor distribution and irrigation mismanagement. This is indicated by the 15-year MVCI spatio-temporal analysis, which shows that the northern part of the scheme experiences characteristic drought during the summer crop season. Although MVCI can be considered a monitoring tool, the index does not deduct the soil water content, and water could be wasted and available in other ways (e.g. water depressions). Spatio-temporal information for ETa is required to better quantify water depletion and establish links between land use and water allocation. However, several RS models have been developed for estimating ETa. Thus, improving the understanding of performance of such models in arid climates, as well as large-scale irrigation schemes, is taken into account in this study. Four different models based on the energy balance method, the Surface Energy Balance Algorithm for Land (SEBAL), Mapping EvapoTranspiration at High Resolution with Internalized Calibration (METRIC™), Simplified Surface Energy Balance (SSEB) and MOD16 ET are applied in order to determine the optimal approach for obtaining ETa. Outputs from these models are compared to actual water balance (WB) estimates during the 2004/05 season at field scale. Several statistical measures are evaluated, and a score is given for each model in order to select the best-performing model. Based on ranking criteria, SSEB gives the best performance and is seen as a suitable operational ETa model for the scheme. SSEB subsequently is applied for summer and winter crop seasons for the period 2000–2014. Unfortunately, one of the limitations faced in the current research is the absence of validation data on a regional scale. Therefore, the assessment focuses on spatial distribution and trends rather than absolute values. As with the MVCI distribution, the seasonal ETa for the Gezira Scheme is higher in the southern and central parts than in the northern part. This confirms the robustness of the developed MVCI. To avoid using absolute values of ETa, the ratio of ETa from agricultural areas (ETagr) to the total evapotranspiration (ET) from the scheme (ETsum) is calculated. The ETagr/ETsum ratio shows a descending trend over recent years, indicating that the water is available but not being utilized for agricultural production. This study shows that SSEB is also useful for identifying the location of water losses on a daily basis. Around 80 channels are identified as having leakage problems for the 2013/14 crop season. Such information is very useful for reducing losses at the scheme. In addition, Rainwater Harvesting (WH) is addressed and found to be applicable as an alternative solution for accounting for rainfall in irrigation. It is seen that these management scenarios could save water and increase the overall efficiency of the scheme. It is possible to save 68 million cubic meters of water per year when the overall irrigation efficiency of the scheme is improved by only 1%. A level of efficiency of 75% is predicted from the proposed management scenarios, which could save about 2.6 billion cubic meters of water per year. In conclusion, the present study has developed an innovative method of identifying the problems of large-scale schemes as well as proposing management scenarios to enhance irrigation water management practice. Improved agricultural water management in terms of crop, water and land management can increase food production, thereby alleviating poverty and hunger in an environmentally sustainable manner.

Background: Great strides have been made in combating malaria, however, the indicators in sub Saharan Africa still do not show promise for elimination in the near future as malaria infections still result in high morbidity and mortality among children. The abundance of the malaria-transmitting mosquito vectors in these regions are driven by climate suitability. In order to achieve malaria elimination by 2030, strengthening of surveillance systems have been advocated. Based on malaria surveillance and climate monitoring, forecasting models may be developed for early warnings. Therefore, in this thesis, we strived to illustrate the use malaria surveillance and climate data for policy and decision making by assessing the association between weather variability (from ground and remote sensing sources) and malaria mortality, and by building malaria admission forecasting models. We further propose an economic framework for integrating forecasts into operational surveillance system for evidence based decisionmaking and resource allocation.  Methods: The studies were based in Asembo, Gem and Karemo areas of the KEMRI/CDC Health and Demographic Surveillance System in Western Kenya. Lagged association of rainfall and temperature with malaria mortality was modeled using general additive models, while distributed lag non-linear models were used to explore relationship between remote sensing variables, land surface temperature(LST), normalized difference vegetation index(NDVI) and rainfall on weekly malaria mortality. General additive models, with and without boosting, were used to develop malaria admissions forecasting models for lead times one to three months. We developed a framework for incorporating forecast output into economic evaluation of response strategies at different lead times including uncertainties. The forecast output could either be an alert based on a threshold, or absolute predicted cases. In both situations, interventions at each lead time could be evaluated by the derived net benefit function and uncertainty incorporated by simulation.  Results: We found that the environmental factors correlated with malaria mortality with varying latencies. In the first paper, where we used ground weather data, the effect of mean temperature was significant from lag of 9 weeks, with risks higher for mean temperatures above 250C. The effect of cumulative precipitation was delayed and began from 5 weeks. Weekly total rainfall of more than 120 mm resulted in increased risk for mortality. In the second paper, using remotely sensed data, the effect of precipitation was consistent in the three areas, with increasing effect with weekly total rainfall of over 40 mm, and then declined at 80 mm of weekly rainfall. NDVI below 0.4 increased the risk of malaria mortality, while day LST above 350C increased the risk of malaria mortality with shorter lags for high LST weeks. The lag effect of precipitation was more delayed for precipitation values below 20 mm starting at week 5 while shorter lag effect for higher precipitation weeks. The effect of higher NDVI values above 0.4 were more delayed and protective while shorter lag effect for NDVI below 0.4. For all the lead times, in the malaria admissions forecasting modelling in the third paper, the boosted regression models provided better prediction accuracy. The economic framework in the fourth paper presented a probability function of the net benefit of response measures, where the best response at particular lead time corresponded to the one with the highest probability, and absolute value, of a net benefit surplus.  Conclusion: We have shown that lagged relationship between environmental variables and malaria health outcomes follow the expected biological mechanism, where presentation of cases follow the onset of specific weather conditions and climate variability. This relationship guided the development of predictive models showcased with the malaria admissions model. Further, we developed an economic framework connecting the forecasts to response measures in situations with considerable uncertainties. Thus, the thesis work has contributed to several important components of early warning systems including risk assessment; utilizing surveillance data for prediction; and a method to identifying cost-effective response strategies. We recommend economic evaluation becomes standard in implementation of early warning system to guide long-term sustainability of such health protection programs.

No description available.

Les données issues de la télédétection couplées à des approches de type système d'information géographique sont d'un grand intérêt potentiel pour l'aménagement forestier. Le but de cette recherche est dans un premier temps de fournir une cartographie utilisable des espèces forestières dominantes à l'échelle du pixel, en utilisant des méthodes éprouvées de la télédétection. Le site d'étude choisi est le versant sud du Mont Ventoux, une montagne méditerranéenne présentant une forte biodiversité forestière. Les travaux relatés dans la première partie ont permis de réaliser des cartographies discriminantes des espèces forestières à partir des données satellitaires (Spot 5) par classification supervisée et non supervisée, en lien avec des relevés terrains. La pertinence de ces méthodes pour la cartographie de la couverture forestière est évaluée et discutée, l'objectif étant d'identifier les conditions optimales en fonction de la résolution spatiale et de la bande spectrale pour la discrimination des espèces forestières majeures du Mont Ventoux. Ce travail nous a ensuite amenés à proposer une méthode originale de mesure de la variabilité de la biodiversité à l'aide de 4 indices classiques : indices de Shannon, de Simpson, de Richesse et de Dominance. L'approche a été appliquée en utilisant deux descripteurs de la biodiversité : l'indice de végétation normalisé (NDVI) et la diversité en espèces forestières. Cette méthode inédite permet, grâce au recours à différentes images de résolutions spatiales imbriquées et à un processus systématique d'agrégation, d'extraire la part de biodiversité (alpha et bêta) due à la structure spatiale, en éliminant l'effet du support spatial, composante déterminante du Modifiable Areal Unit Problem (MAUP). Nous discutons également dans cette recherche de la capacité de notre méthode à extraire, une « échelle pertinente » de mesure de la diversité.