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Sensitivity analysis assessment of remotely based vegetation indices to improve water resources management

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  • Mohamed Elhag

Abstract

Normalized Difference Vegetation Index (NDVI) is estimated from Landsat 8 sensor acquired in June 2013 to drive four different water-related indices calculated as NDVI derivatives. Different vegetation indices (VIs) have been extracted exclusively in estimation of different VIs: Leaf Area Index, Water Supply Vegetation Index, Crop Water Shortage Index, and Drought Severity Index in addition to estimation of daily evapotranspiration (ET). Sensitivity analysis assesses the contributions of the inputs to the total uncertainty in the analysis outcomes. Vegetation indices are complex and intercepted, therefore the interceptions of the five different vegetation indices are considered in the current study. A comparative analysis of Gaussian process emulators for performing global sensitivity analysis was used to conduct a variance-based sensitivity analysis to identify which uncertain inputs are driving the output uncertainty. The results showed that the interconnections between different VIs vary, but the extent of the features sensitivity is uncertain. Findings from the current work conducted are anticipated to contribute decisively toward an inclusive VIs assessment of its overall verification. Daily ET is the less sensitive and more certain index followed by Drought Vegetation Index. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • Mohamed Elhag, 2014. "Sensitivity analysis assessment of remotely based vegetation indices to improve water resources management," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 16(6), pages 1209-1222, December.
    • Handle: RePEc:spr:endesu:v:16:y:2014:i:6:p:1209-1222
    DOI: 10.1007/s10668-014-9522-0
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    References listed on IDEAS

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    1. Petropoulos, G. & Wooster, M.J. & Carlson, T.N. & Kennedy, M.C. & Scholze, M., 2009. "A global Bayesian sensitivity analysis of the 1d SimSphere soil–vegetation–atmospheric transfer (SVAT) model using Gaussian model emulation," Ecological Modelling, Elsevier, vol. 220(19), pages 2427-2440.
    2. Mohamed Elhag & Aris Psilovikos & Ioannis Manakos & Kostas Perakis, 2011. "Application of the Sebs Water Balance Model in Estimating Daily Evapotranspiration and Evaporative Fraction from Remote Sensing Data Over the Nile Delta," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(11), pages 2731-2742, September.
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    5. Mohamed Elhag & Aris Psilovikos & Maria Sakellariou-Makrantonaki, 2013. "Land use changes and its impacts on water resources in Nile Delta region using remote sensing techniques," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 15(5), pages 1189-1204, October.
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    1. Sadhan Malik & Subodh Chandra Pal & Biswajit Das & Rabin Chakrabortty, 2020. "Assessment of vegetation status of Sali River basin, a tributary of Damodar River in Bankura District, West Bengal, using satellite data," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(6), pages 5651-5685, August.
    2. Riska Ayu Purnamasari & Tofael Ahamed & Ryozo Noguchi, 2019. "Land suitability assessment for cassava production in Indonesia using GIS, remote sensing and multi-criteria analysis," Asia-Pacific Journal of Regional Science, Springer, vol. 3(1), pages 1-32, February.

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