Quantitative estimation of drought risk in Ukraine using satellite data

In this paper, we focus on quantitative drought risk assessment using satellite data. Methods of the extreme value theory (EVT) are applied for a time-series of vegetation health index (VHI) derived from NOAA satellites in order to provide drought hazard mapping. For this, a Poisson-GP (Generalized Pareto) model is applied for modelling VHI extreme values. The model allows estimation and mapping of return periods of different categories of drought severity. An approach to economical risk assessment due to droughts is presented. The derived drought hazard map is integrated with high resolution crop map to provide final estimates of risk. The proposed approach is implemented for quantitative assessment of drought risk for the Kyiv region in Ukraine.

[1]  M. Svoboda.  An Introduction to the Drought Monitor , 2000 .

[2]  Javier Gallego,et al.  Efficiency Assessment of Different Approaches to Crop Classification Based on Satellite and Ground Observations , 2012 .

[3]  Sergio M. Vicente-Serrano,et al.  Mapping the hazard of extreme rainfall by peaks-over-threshold extreme value analysis and spatial regression techniques , 2006 .

[4]  N. Guttman.  COMPARING THE PALMER DROUGHT INDEX AND THE STANDARDIZED PRECIPITATION INDEX 1 , 1998 .

[5]  Nataliia Kussul,et al.  Winter wheat yield forecasting in Ukraine based on Earth observation, meteorological data and biophysical models , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[6]  Nataliia Kussul,et al.  Flood Hazard and Flood Risk Assessment Using a Time Series of Satellite Images: A Case Study in Namibia , 2014, Risk analysis : an official publication of the Society for Risk Analysis.

[7]  Francisco Javier Gallego,et al.  Efficiency assessment of using satellite data for crop area estimation in Ukraine , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[8]  Konstantinos Perakis,et al.  Assessment of crop damage using space remote sensing and GIS , 2002 .

[9]  Hartwig H. Hochmair,et al.  Mapping return levels of absolute NDVI variations for the assessment of drought risk in Ethiopia , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[10]  Francisco Javier Gallego,et al.  Crop area estimation in Ukraine using satellite data within the MARS project , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[11]  Andrey Yu. Shelestov,et al.  Disaster Risk Assessment Based on Heterogeneous Geospatial Information , 2010 .

[12]  Wayne C. Palmer,et al.  Keeping Track of Crop Moisture Conditions, Nationwide: The New Crop Moisture Index , 1968 .

[13]  Olga Kussul,et al.  Winter Wheat Yield Forecasting: a Comparative Analysis of Results of Regression and Biophysical Models , 2013 .

[14]  B. Shafer,et al.  Development of a surface water supply index (SWSI) to assess the severity of drought conditions in snowpack runoff areas , 1982 .

[15]  A.Yu. Shelestov,et al.  Geospatial information system for agricultural monitoring , 2013 .

[16]  V. Singh,et al.  A review of drought concepts , 2010 .

[17]  Nataliya N. Kussul.  Neural Networks Learning Using Method of Fuzzy Ellipsoidal Estimates , 2001 .

[18]  Olga Kussul,et al.  Analysis of Applicability of Neural Networks for Classification of Satellite Data , 2007 .

[19]  A. Yu. Shelestov,et al.  Using the fuzzy-ellipsoid method for robust estimation of the state of a grid system node , 2008 .

[20]  Ladislav Hluchý,et al.  The Data Fusion Grid Infrastructure: Project Objectives and Achievements , 2010, Comput. Informatics.

[21]  Nataliia Kussul,et al.  Assessment of relative efficiency of using MODIS data to winter wheat yield forecasting in Ukraine , 2013, 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS.

[22]  Wei Guo,et al.  Global and regional drought dynamics in the climate warming era , 2013 .

[23]  G. Huffman,et al.  The TRMM Multi-Satellite Precipitation Analysis (TMPA) , 2010 .