气候变化领域集成服务门户(CCPortal): Forecasting vegetation condition for drought early warning systems in pastoral communities in Kenya

CCPortal

DOI	10.1016/j.rse.2020.111886
	Forecasting vegetation condition for drought early warning systems in pastoral communities in Kenya
	Barrett A.B.; Duivenvoorden S.; Salakpi E.E.; Muthoka J.M.; Mwangi J.; Oliver S.; Rowhani P.
发表日期	2020
ISSN	00344257
卷号	248
英文摘要	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. © 2020 Elsevier Inc.
英文关键词	Disaster risk reduction; Drought; Early warning systems; Forecasting; Landsat; MODIS
语种	英语
scopus关键词	Forecasting; Hazards; Regression analysis; Vegetation; Drought management; Early Warning System; Early warning systems; Pastoral communities; Socio-economic indicators; Vegetation condition; Vegetation condition indices; Vulnerable communities; Drought; cost analysis; drought; early warning system; forecasting method; Landsat; livelihood; MODIS; numerical model; pastoralism; satellite altimetry; socioeconomic impact; vegetation dynamics; vegetation index; Kenya
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179194
作者单位	The Data Intensive Science Centre, Department of Physics and Astronomy, University of Sussex, Brighton, BN1 9QH, United Kingdom; Sackler Centre for Consciousness Science, Department of Informatics, University of Sussex, Brighton, BN1 9QJ, United Kingdom; Astronomy Centre, Department of Physics and Astronomy, University of Sussex, Brighton, BN1 9QH, United Kingdom; School of Global Studies, Department of Geography, University of Sussex, Brighton, BN1 9QJ, United Kingdom; The National Drought Management Authority (NDMA), Lonrho House, Nairobi, Kenya
推荐引用方式 GB/T 7714	Barrett A.B.,Duivenvoorden S.,Salakpi E.E.,et al. Forecasting vegetation condition for drought early warning systems in pastoral communities in Kenya[J],2020,248.
APA	Barrett A.B..,Duivenvoorden S..,Salakpi E.E..,Muthoka J.M..,Mwangi J..,...&Rowhani P..(2020).Forecasting vegetation condition for drought early warning systems in pastoral communities in Kenya.Remote Sensing of Environment,248.
MLA	Barrett A.B.,et al."Forecasting vegetation condition for drought early warning systems in pastoral communities in Kenya".Remote Sensing of Environment 248(2020).