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|Title:||Remote sensing based indices for drought assessment in the east mediterranean region||Authors:||Loulli, Eleni
Hadjimitsis, Diofantos G.
|Keywords:||Desertification;Drought;Drought index;Drought monitoring||Category:||Civil Engineering||Field:||Engineering and Technology||Issue Date:||Sep-2018||Source:||Remote Sensing for Agriculture, Ecosystems, and Hydrology XX, 2018, 10-13 September, Berlin, Germany||Conference:||Remote Sensing for Agriculture, Ecosystems, and Hydrology XX||Abstract:||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).||URI:||http://ktisis.cut.ac.cy/handle/10488/13493||DOI:||10.1117/12.2325331||Rights:||© SPIE.||Type:||Conference Papers|
|Appears in Collections:||Δημοσιεύσεις σε συνέδρια/Conference papers|
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