Forecasting vegetation condition for drought early warning systems in pastoral communities in Kenya
Forecasting vegetation condition for drought early warning systems in pastoral communities in Kenya
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DOI：
10.1016/j.rse.2020.111886
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发表时间：
2019-11
影响因子：
13.5
通讯作者：
A. Barrett;S. Duivenvoorden;Edward E. Salakpi;James M. Muthoka;J. Mwangi;Seb Oliver;P. Rowhani
中科院分区：
文献类型：
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作者：
A. Barrett;S. Duivenvoorden;Edward E. Salakpi;James M. Muthoka;J. Mwangi;Seb Oliver;P. Rowhani
干旱是撒哈拉以南非洲经常发生的灾害，可能造成巨大的社会经济代价。根据预警系统提供的警报及早采取行动，有可能提供实质性的缓解，减少财政和人力成本。然而，现有的预警系统往往只监测当前的干旱环境和社会经济指标，而不是预测未来的指标，因此在实践中并不总是及时有效。在这里，我们提出了一种新的方法来预测基于卫星的植被状况的指标。具体来说，我们专注于3个月的植被状况指数（VCI3M）在肯尼亚的畜牧业生计区，这是肯尼亚国家干旱管理局（NDMA）使用的指标。利用中分辨率成像光谱仪和陆地卫星的数据，我们采用线性自回归和高斯过程建模方法，并提前几周展示了较高的预测技巧。作为基准，我们预测了NDMA使用的干旱警报标记（VCI3M<35）。我们的两个模型都能够提前四周预测这个警报标记，命中率约为89%，误报率约为4%，或提前六周分别为81%和6%。因此，这里开发的方法可以很好地和充分地提前确定不断恶化的植被状况，以帮助灾害风险管理人员及早采取行动，支持脆弱社区，并限制干旱灾害的影响。
Droughts are a recurring hazard in sub-Saharan Africa, that can wreak huge socioeconomic costs. Acting early based on alerts provided by early warning systems (EWS) can potentially provide substantial mitigation, reducing the financial and human cost. However, existing EWS tend only to monitor current, rather than forecast future, environmental and socioeconomic indicators of drought, and hence are not always sufficiently timely to be effective in practice. Here we present a novel method for forecasting satellite-based indicators of vegetation condition. Specifically, we focused on the 3-month Vegetation Condition Index (VCI3M) over pastoral livelihood zones in Kenya, which is the indicator used by the Kenyan National Drought Management Authority (NDMA). Using data from MODIS and Landsat, we apply linear autoregression and Gaussian process modelling methods and demonstrate high forecasting skill several weeks ahead. As a bench mark we predicted the drought alert marker used by NDMA (VCI3M<35). Both of our models were able to predict this alert marker four weeks ahead with a hit rate of around 89% and a false alarm rate of around 4%, or 81% and 6% respectively six weeks ahead. The methods developed here can thus identify a deteriorating vegetation condition well and sufficiently in advance to help disaster risk managers act early to support vulnerable communities and limit the impact of a drought hazard.