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Sea Surface Temperature changes analysis, an Essential Climate Variable for Ecosystem Services provisioning

机译:海面温度变化分析,生态系统服务供应的必要气候变量

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Sea Surface Temperature is an Essential Climate Variable (ECV) that allows an effective quantitative estimation of recent changes in large marine ecosystem like the Mediterranean Sea. Sea Surface Temperature (SST) continues to rise, threatening marine ecosystem status and Ecosystem Services (ESS) provisioning. One of the questions that multitemporal analysis of Earth Observation (EO) time series should address is the response to variations in SST spatial distribution due to climate change. Ecosystem indicators like the fish growth rates across Mediterranean regions reveal temporal trends and regional variability. This study addresses the changes of the SST of the Mediterranean Sea "Large Marine Ecosystem" over the last three decades in order to evaluate trends, identify spatial and temporal patterns of SST variability from multitemporal analysis of EO products. Time series of daily SST estimated for the period 1982-2016 from multi-sensor satellite data were collected from operational Copernicus Marine Environment Monitoring Service (CMEMS). A wide range of statistical approaches are considered, like Seasonal Trend decomposition, Empirical Orthogonal Function, Self-Organizing Maps. Focusing on the thermal habitat of fish species, a fish growth model is used to reveal different scenarios in the potential growth of fish populations under past and current conditions as well as future climate projections. Results indicate that in the past three decades the eastern part of the Mediterranean Sea experienced greater SST increase than the western part, producing different scenarios of fish growth rates across the Mediterranean regions.
机译:海表面温度是必不可少的气候变量(ECV),允许有效地估计大型海洋生态系统的最近变化,如地中海。海面温度(SST)继续上升,威胁海洋生态系统状态和生态系统服务(ESS)供应。地球观测(EO)时间序列的多态分析应该解决的问题是由于气候变化导致的SST空间分布变化的响应。生态系统指标,如地中海地区的鱼类增长率显示了时间趋势和区域变异性。本研究解决了地中海“大型海洋生态系统”在过去三十年中的变化,以评估趋势,识别来自EO产品的多发性分析的SST变异的空间和时间模式。从多传感器卫星数据的1982-2016期间估计的每日SST的时间系列从运营哥白尼海洋环境监测服务(CMEMS)收集。考虑了广泛的统计方法,如季节性趋势分解,经验正交功能,自组织地图。专注于鱼类的热栖息地,鱼类生长模型用于揭示过去和当前条件下的鱼群的潜在生长以及未来的气候预测中的不同情景。结果表明,在过去三十年中,地中海的东部经历了比西方的SST增加更大,在地中海地区产生了不同的鱼类增长率场景。

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