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Accueil du site → Doctorat → États-Unis → 2015 → A Simple Model to Predict Agricultural Drought through the Relationship between Normalized Difference Vegetation Index (NDVI) and Precipitation in Arid- and Semi- Arid Climates

George Mason University (2015)

A Simple Model to Predict Agricultural Drought through the Relationship between Normalized Difference Vegetation Index (NDVI) and Precipitation in Arid- and Semi- Arid Climates

Yagci, Ali Levent

Titre : A Simple Model to Predict Agricultural Drought through the Relationship between Normalized Difference Vegetation Index (NDVI) and Precipitation in Arid- and Semi- Arid Climates

Auteur : Yagci, Ali Levent

Université de soutenance  : George Mason University

Grade : Doctor of Philosophy (PhD) 2015

Résumé
Drought is a recurring natural disaster that causes major damage on agriculture, ecosystems and economy. Accurate and timely drought prediction enables early preparations in order to mitigate its negative impacts. Satellite remote sensing data and methods have been proven to be successful at drought detection and monitoring from space. Among these methods, vegetation indices such as the Normalized Difference Vegetation Index (NDVI) and Vegetation Condition Index (VCI) have been widely used to monitor droughts worldwide. In arid and semi-arid climate regions where water is the sole limiting factor to vegetation growth, NDVI lags 1 or 2 months behind precipitation depending on plant types. This study was conducted in a semi-arid climate, Middle Brazos-Clear Fork Basin, Texas, US. The results indicated that NDVI was satisfactorily predicted 16 days in advance by taking advantage of this exclusive time lag between precipitation and NDVI, and then VCI was calculated using this predicted NDVI and historical NDVI values. The mean absolute errors (MAE) and root mean square errors (RMSE) between observed and predicted NDVIs were as little as 0.021and 0.026 for NDVI (NDVI range, 0-1), and 6.26 and 8.08 for VCIs (VCI range, 0-100), respectively.

Présentation (ProQuest)

Page publiée le 29 mai 2021