A spatial regression procedure for evaluating the relationship between AVHRR-NDVI and climate in the northern Great Plains

Abstract

The relationship between vegetation and climate in the grassland and cropland of the northern US Great Plains was investigated with Normalized Difference Vegetation Index (NDVI) (1989–1993) images derived from the Advanced Very High Resolution Radiometer (AVHRR), and climate data from automated weather stations. The relationship was quantified using a spatial regression technique that adjusts for spatial autocorrelation inherent in these data. Conventional regression techniques used frequently in previous studies are not adequate, because they are based on the assumption of independent observations. Six climate variables during the growing season; precipitation, potential evapotranspiration, daily maximum and minimum air temperature, soil temperature, solar irradiation were regressed on NDVI derived from a 10-km weather station buffer. The regression model identified precipitation and potential evapotranspiration as the most significant climatic variables, indicating that the water balance is the most important factor controlling vegetation condition at an annual timescale. The model indicates that 46% and 24% of variation in NDVI is accounted for by climate in grassland and cropland, respectively, indicating that grassland vegetation has a more pronounced response to climate variation than cropland. Other factors contributing to NDVI variation include environmental factors (soil, groundwater and terrain), human manipulation of crops, and sensor variation.

Acknowledgments

This paper is based on Lei Ji's doctoral dissertation. The authors would like to thank Elizabeth Walter-Shea, Donald Rundquist, James Merchant, and Andres Viña for useful suggestions on the manuscript.

Notes

1The NDVI is normally a decimal value ranging from −1.0 to 1.0. But for an image, it is usually scaled to an 8-bit data range. The NDVI data range of −1.0 to 1.0 was scaled linearly to the range 0 to 200, where computed −1.0 equals 0, computed 0 equals 100, and computed 1.0 equals 200. Rescaled NDVI values are used in this research.