Applying Astronomy Data Analysis to enhance disaster forecasting

应用天文数据分析增强灾害预报

• 批准号: ST/R004811/1
• 负责人: Sebastian Oliver
• 金额: $ 12.8万
• 依托单位: University of Sussex
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=ST%2FR004811%2F1
• 关键词: Applying; Astronomy; Data; Analysis; enhance

Livestock accounts for 37.5% of Kenya's land area, 12% of its GDP and 40% of its agricultural sector, but is susceptible to frequent droughts and periods of overgrazing. In this context, this project will assess the potential of new Earth observation datasets to deliver near real-time monitoring and prediction of useful and accessible biomass for pastoralism.Drought and flood events are a major threat in sub-Saharan Africa (SSA) causing substantial losses of life, assets and livelihoods, and weakened national economic performance. Hazard early warning and disaster risk preparedness actions can be effective in reducing these losses (as much as 20 times more effective than post-disaster relief). In this project we will apply advanced data analysis techniques used in astronomy to facilitate improved hazard early warning models in Kenya. This pilot project brings together STFC funded Astronomers at University of Sussex with a strong track record in data analysis with a world-leading interdisciplinary team in Climate Change and Developmental studies. Several global to regional pasture monitoring systems exist that are based on Earth observation data, which are used in early warning systems. These systems tend to rely on coarse resolution data (250m - 8km) to provide near real-time information on vegetation health, and are combined with mechanistic models or expert knowledge to forecast seasonal outcomes. However, this spatial scale is unable to adequately distinguish pastures from scrublands, small farms and woody vegetation, and is unable to provide meaningful information on the onset of vegetation stresses. This project will use data from the Copernicus Sentinel mission (data provided every 5-12 days at 10-20m resolution) with key information on vegetation state.The ultimate outcome of this research will support pastoralists communities Kenya, to decide the suitability and location of pastureland for their various livestock through: a) improved understanding of spatio-temporal distribution of pastures; b) improved understanding of ecological changes and resilience of pastures; and c) near-future predictions of pasture suitability. This will enhance their livelihood resilience in the wake of large and extensive droughts, overgrazing, and land cover change.

牲畜占肯尼亚土地面积的37.5％，占其GDP的12％和其农业部门的40％，但容易受到频繁的干旱和过度放弃时期的影响。在这种情况下，该项目将评估新的地球观测数据集的潜力，以实时监测和对牧民的有用和可访问的生物质的预测。干旱和洪水事件是撒哈拉以南非洲（SSA）（SSA）的主要威胁，从而造成了实质性损失，资产，资产和生活，谋生和谋生和削弱国家经济表现。危害预警和防灾风险行动可以有效减少这些损失（比灾后救济高20倍）。在该项目中，我们将应用用于天文学中的高级数据分析技术，以促进肯尼亚的危害预警模型。该试点项目将苏塞克斯大学的STFC资助的天文学家汇集在一起​​，并在数据分析方面具有良好的往绩，并在气候变化和发展研究中拥有世界领先的跨学科团队。存在基于地球观察数据的几种全球至区域牧场监测系统，这些数据用于预警系统。这些系统倾向于依靠粗分辨率数据（250m -8公里）来提供有关植被健康的近乎实时信息，并与机械模型或专家知识相结合以预测季节性结果。但是，这种空间尺度无法充分区分牧场和灌木丛，小型农场和木质植被，并且无法提供有关植被胁迫发作的有意义的信息。该项目将使用哥白尼前哨任务中的数据（以10-20m分辨率提供每5-12天每5-12天提供的数据），并提供有关植被状态的关键信息。这项研究的最终结果将支持肯尼亚的牧民社区，以确定牧场的适合性和位置，以通过以下方式通过以下方式：a）对Spatio-temporporal分布的牧师分布的了解。 b）改善对牧场的生态变化和韧性的理解； c）牧场适用性的近乎未来的预测。这将增强他们的生计韧性，随着大型干旱，过度放牧和土地覆盖的变化。

项目成果

Forecasting vegetation condition for drought early warning systems in pastoral communities in Kenya

• DOI: 10.1016/j.rse.2020.111886
• 发表时间: 2019-11
• 期刊: Remote Sensing of Environment
• 影响因子: 13.5
• 作者: A. Barrett;S. Duivenvoorden;Edward E. Salakpi;James M. Muthoka;J. Mwangi;Seb Oliver;P. Rowhani