Using AVHRR-based vegetation indices for drought monitoring in the Northwest of Iran

In order to evaluate the capability of NOAA-AVHRR data for drought monitoring in the northwest of Iran having cold semi-arid climate, a study plan was designed involving the production of normalized difference vegetation index (NDVI) and vegetation condition index (VCI) indices and correlating their values to precipitation data. Raw AVHRR images were processed and geometric and radiometric corrections were performed. Seven-day maximum NDVI maps were produced and VCI was calculated using the maximum and minimum NDVI values for the same time period. Precipitation statistics from 19 synoptic meteorological stations were collected. The study covered a five-year time period with three consecutive months in the growing season. Pearson correlation was performed to correlate NDVI and VCI values to precipitation data. Different time lag schemes were tried and the highest correlation coefficients (r values) were obtained while correlating NDVI and VCI to three-month (current plus last two months) precipitation. Better agreement was observed between NDVI and precipitation as compared with that between VCI and precipitation in individual stations. Good correlations were also obtained between average NDVI and VCI of the study area and average three-month precipitation. The results indicated that NOAA-AVHRR derived NDVI well reflects precipitation fluctuations in the study area promising a possibility for early drought awareness necessary for drought risk management.

[1]  T. M. Lillesand,et al.  Remote Sensing and Image Interpretation , 1980 .

[2]  W. Dulaney,et al.  Normalized difference vegetation index measurements from the Advanced Very High Resolution Radiometer , 1991 .

[3]  Stuart E. Marsh,et al.  Comparison of multi-temporal NOAA-AVHRR and SPOT-XS satellite data for mapping land-cover dynamics in the West African Sahel , 1992 .

[4]  K. Price,et al.  Temporal responses of NDVI to precipitation and temperature in the central Great Plains, USA , 2003 .

[5]  A. Gitelson,et al.  Using AVHRR data for quantitive estimation of vegetation conditions: Calibration and validation , 1998 .

[6]  Murat Karabulut,et al.  An Examination of Relationships Between Vegetation and Rainfall Using Maximum Value Composite AVHRR-NDVI Data , 2003 .

[7]  C. Tucker Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[8]  Jesslyn F. Brown,et al.  Measuring phenological variability from satellite imagery , 1994 .

[9]  Sharon E. Nicholson,et al.  A STUDY OF RAINFALL AND VEGETATION DYNAMICS IN THE AFRICAN SAHEL USING NORMALIZED DIFFERENCE VEGETATION INDEX , 1990 .

[10]  F. Kogan,et al.  Drought Monitoring and Corn Yield Estimation in Southern Africa from AVHRR Data , 1998 .

[11]  A. S. Belward,et al.  Scale considerations in vegetation monitoring using AVHRR data , 1992 .

[12]  F. Kogan Application of vegetation index and brightness temperature for drought detection , 1995 .

[13]  R. Lunetta,et al.  A change detection experiment using vegetation indices. , 1998 .

[14]  J. Campbell Introduction to remote sensing , 1987 .

[15]  Dennis Nazarenko,et al.  RADARSAT: First Images , 1995 .

[16]  Liping Di,et al.  Modelling relationships between NDVI and precipitation during vegetative growth cycles , 1994 .

[17]  Jürgen Vogt,et al.  DROUGHT MONITORING FROM SPACE , 2000 .

[18]  James W. Merchant,et al.  An assessment of AVHRR/NDVI-ecoclimatological relations in Nebraska, U.S.A. , 1997 .