Arid
DOI10.1117/12.2325331
Remote Sensing Based Indices for Drought Assessment in the East Mediterranean Region
Loulli, Eleni; Hadjimitsis, Diofantos G.
通讯作者Loulli, Eleni
会议名称Conference on Remote Sensing for Agriculture, Ecosystems, and Hydrology XX
会议日期SEP 10-13, 2019
会议地点Berlin, GERMANY
英文摘要

This study aims at reviewing existing remote sensing approaches to assess drought impact on desertification in the East Mediterranean region. Drought and desertification are interconnected phenomena. The World Meteorological Organization (WMO) defines that an area is affected by drought when the annual precipitation is lower than 60 % of the normal values, at least during 2 consecutive years in more than 50 % of its area. Drought is a phenomenon that may trigger or exacerbate desertification. Desertification is usually reported as the process during which land becomes more arid and loses its vegetation, water bodies (lakes, streams), and wildlife. Being one of the major causes of desertification, drought is a complex phenomenon and its monitoring is crucial for early warning and risk management of desertification. A number of approaches are possible for assessing drought. This paper reviews remotely-sensed drought indices that are particularly relevant to the East Mediterranean Region, discussing their strengths and weaknesses, as well as their present challenges. As the East Mediterranean Region is dominated by semi-arid to arid climates, focus is here given to methods applied to assess drought in semi-arid or arid regions. The present paper analyses the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI), the Normalized Difference Drought Index (NDDI), the Vegetation Condition Index (VCI), the Temperature Condition Index (TCI), the Vegetation Health Index (VHI) and the Composite Drought Index (CDI). For their validation, the indices need to be compared to ancillary data, recorded at meteorological stations or acquired from in-situ measurements. Thus, the paper suggests Goodness-of-fit criteria, which correlate the derived data spatially and temporally. Examples of such criteria are the Pearson product-moment correlation coefficient r, the coefficient of determination R2, the Root Mean Squared Error (RMSE) and the Nash Sutcliffe Efficiency (NSE).


英文关键词drought drought index desertification drought monitoring
来源出版物REMOTE SENSING FOR AGRICULTURE, ECOSYSTEMS, AND HYDROLOGY XX
ISSN0277-786X
EISSN1996-756X
出版年2018
卷号10783
EISBN978-1-5106-2150-3
出版者SPIE-INT SOC OPTICAL ENGINEERING
类型Proceedings Paper
语种英语
国家Cyprus
收录类别CPCI-S
WOS记录号WOS:000453071600025
WOS关键词VEGETATION
WOS类目Environmental Sciences ; Remote Sensing ; Optics
WOS研究方向Environmental Sciences & Ecology ; Remote Sensing ; Optics
资源类型会议论文
条目标识符http://119.78.100.177/qdio/handle/2XILL650/307754
作者单位Cyprus Univ Technol, Sch Engn & Technol, Dept Civil Engn & Geomat, Limassol, Cyprus
推荐引用方式
GB/T 7714
Loulli, Eleni,Hadjimitsis, Diofantos G.. Remote Sensing Based Indices for Drought Assessment in the East Mediterranean Region[C]:SPIE-INT SOC OPTICAL ENGINEERING,2018.
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