Applying Astronomy Data Analysis to enhance disaster forecasting

畜牧业占肯尼亚土地面积的37.5%，占国内生产总值的12%，占农业部门的40%，但容易受到频繁干旱和过度放牧的影响。在这方面，该项目将评估新的地球观测数据集在提供对畜牧业有用和可获得的生物量的近实时监测和预测方面的潜力，干旱和洪水事件是撒哈拉以南非洲的一个主要威胁，造成大量生命、资产和生计损失，削弱了国家经济绩效。灾害预警和灾害风险防备行动可有效减少这些损失（比灾后救济的效果高20倍）。在这个项目中，我们将应用天文学中使用的先进数据分析技术，以促进改进肯尼亚的灾害预警模型。这个试点项目汇集了苏塞克斯大学STFC资助的天文学家，他们在气候变化和发展研究方面拥有世界领先的跨学科团队，在数据分析方面有着良好的记录。现有若干全球至区域牧场监测系统，这些系统以地球观测数据为基础，用于预警系统。这些系统往往依赖于粗分辨率数据（250米至8公里），以提供关于植被健康的近实时信息，并结合机械模型或专家知识来预测季节性结果。然而，这种空间尺度无法充分区分牧场和灌木丛、小农场和木本植被，也无法提供关于植被压力发生的有意义的信息。该项目将使用哥白尼哨兵使命的数据这项研究的最终成果将支持肯尼亚牧民社区，通过以下方式决定牧场对各种牲畜的适宜性和位置：a）更好地了解牧场的时空分布; B）提高对生态变化和牧场恢复力的认识;以及c）对牧场适宜性的近期预测。这将增强他们在大规模干旱、过度放牧和土地覆盖变化后的生计复原力。

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.