39 resultados para GIS and Teledetection


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Mara is a transboundary river located in Kenya and Tanzania and considered to be an important life line to the inhabitants of the Mara-Serengeti ecosystem. It is also a source of water for domestic water supply, irrigation, livestock and wildlife. The alarming increase of water demand as well as the decline in the river flow in recent years has been a major challenge for water resource managers and stakeholders. This has necessitated the knowledge of the available water resources in the basin at different times of the year. Historical rainfall, minimum and maximum stream flows were analyzed. Inter and intra-annual variability of trends in streamflow are discussed. Landsat imagery was utilized in order to analyze the land use land cover in the upper Mara River basin. The semi-distributed hydrological model, Soil and Water Assessment Tool (SWAT) was used to model the basin water balance and understand the hydrologic effect of the recent land use changes from forest-to-agriculture. The results of this study provided the potential hydrological impacts of three land use change scenarios in the upper Mara River basin. It also adds to the existing literature and knowledge base with a view of promoting better land use management practices in the basin.

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With the flow of the Mara River becoming increasingly erratic especially in the upper reaches, attention has been directed to land use change as the major cause of this problem. The semi-distributed hydrological model Soil and Water Assessment Tool 5 (SWAT) and Landsat imagery were utilized in the upper Mara River Basin in order to 1) map existing field scale land use practices in order to determine their impact 2) determine the impacts of land use change on water flux; and 3) determine the impacts of rainfall (0%, ±10% and ±20%) and air temperature variations (0% and +5%) based on the Intergovernmental Panel on Climate Change projections on the water flux of the 10 upper Mara River. This study found that the different scenarios impacted on the water balance components differently. Land use changes resulted in a slightly more erratic discharge while rainfall and air temperature changes had a more predictable impact on the discharge and water balance components. These findings demonstrate that the model results 15 show the flow was more sensitive to the rainfall changes than land use changes. It was also shown that land use changes can reduce dry season flow which is the most important problem in the basin. The model shows also deforestation in the Mau Forest increased the peak flows which can also lead to high sediment loading in the Mara River. The effect of the land use and climate change scenarios on the sediment and 20 water quality of the river needs a thorough understanding of the sediment transport processes in addition to observed sediment and water quality data for validation of modeling results.

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Some of the most valued natural and cultural landscapes on Earth lie in river basins that are poorly gauged and have incomplete historical climate and runoff records. The Mara River Basin of East Africa is such a basin. It hosts the internationally renowned Mara-Serengeti landscape as well as a rich mixture of indigenous cultures. The Mara River is the sole source of surface water to the landscape during the dry season and periods of drought. During recent years, the flow of the Mara River has become increasingly erratic, especially in the upper reaches, and resource managers are hampered by a lack of understanding of the relative influence of different sources of flow alteration. Uncertainties about the impacts of future climate change compound the challenges. We applied the Soil Water Assessment Tool (SWAT) to investigate the response of the headwater hydrology of the Mara River to scenarios of continued land use change and projected climate change. Under the data-scarce conditions of the basin, model performance was improved using satellite-based estimated rainfall data, which may also improve the usefulness of runoff models in other parts of East Africa. The results of the analysis indicate that any further conversion of forests to agriculture and grassland in the basin headwaters is likely to reduce dry season flows and increase peak flows, leading to greater water scarcity at critical times of the year and exacerbating erosion on hillslopes. Most climate change projections for the region call for modest and seasonally variable increases in precipitation (5–10 %) accompanied by increases in temperature (2.5–3.5 °C). Simulated runoff responses to climate change scenarios were non-linear and suggest the basin is highly vulnerable under low (−3 %) and high (+25 %) extremes of projected precipitation changes, but under median projections (+7 %) there is little impact on annual water yields or mean discharge. Modest increases in precipitation are partitioned largely to increased evapotranspiration. Overall, model results support the existing efforts of Mara water resource managers to protect headwater forests and indicate that additional emphasis should be placed on improving land management practices that enhance infiltration and aquifer recharge as part of a wider program of climate change adaptation.

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A group of four applications including Top 20 Pedestrian Crash Locations: This application is designed to display top 20 pedestrian crash locations into both map- view and detailed information view. FDOT Crash Reporting Tool: This application is designed to simplify the usage and sharing of CAR data. The application can load raw data from CAR and display it into a web map interface. FDOT Online Document Portal: This application is designed for FDOT project managers to be able to share and manage documents through a user friendly, GIS enable web interface GIS Data Collection for Pedestrian Safety Tool: FIU-GIS Center was responsible for data collection and processing work for the project of Pedestrian Safety Tool Project. The outcome of this task is present by a simple web-GIS application design to host GIS by projects.

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Geoportal allows users to find, search and share data.

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With the exponential growth of the usage of web-based map services, the web GIS application has become more and more popular. Spatial data index, search, analysis, visualization and the resource management of such services are becoming increasingly important to deliver user-desired Quality of Service. First, spatial indexing is typically time-consuming and is not available to end-users. To address this, we introduce TerraFly sksOpen, an open-sourced an Online Indexing and Querying System for Big Geospatial Data. Integrated with the TerraFly Geospatial database [1-9], sksOpen is an efficient indexing and query engine for processing Top-k Spatial Boolean Queries. Further, we provide ergonomic visualization of query results on interactive maps to facilitate the user’s data analysis. Second, due to the highly complex and dynamic nature of GIS systems, it is quite challenging for the end users to quickly understand and analyze the spatial data, and to efficiently share their own data and analysis results with others. Built on the TerraFly Geo spatial database, TerraFly GeoCloud is an extra layer running upon the TerraFly map and can efficiently support many different visualization functions and spatial data analysis models. Furthermore, users can create unique URLs to visualize and share the analysis results. TerraFly GeoCloud also enables the MapQL technology to customize map visualization using SQL-like statements [10]. Third, map systems often serve dynamic web workloads and involve multiple CPU and I/O intensive tiers, which make it challenging to meet the response time targets of map requests while using the resources efficiently. Virtualization facilitates the deployment of web map services and improves their resource utilization through encapsulation and consolidation. Autonomic resource management allows resources to be automatically provisioned to a map service and its internal tiers on demand. v-TerraFly are techniques to predict the demand of map workloads online and optimize resource allocations, considering both response time and data freshness as the QoS target. The proposed v-TerraFly system is prototyped on TerraFly, a production web map service, and evaluated using real TerraFly workloads. The results show that v-TerraFly can accurately predict the workload demands: 18.91% more accurate; and efficiently allocate resources to meet the QoS target: improves the QoS by 26.19% and saves resource usages by 20.83% compared to traditional peak load-based resource allocation.