Application of NOAA-AVHRR NDVI and Rainfall Time-Series to Assess Desertification in Central Asia

Pavel A. Propastin ^a,b, Martin Kappas ^a and Nadia R. Muratova ^b

^a Department of Geography, Georg-August University, Göttingen, Germany
e-mails: ppavel@gmx.de , mkappas@uni-goettingen.de

^b Laboratory of Remote Sensing and Image Analysis, Kazakh Academy of Science, Almaty, Kazakhstan
e-mail: nmuratova@hotmail.com
 

Abstract

The primary objective of this study was to assess a degradation of vegetation cover in Central Asia and discriminate between climate-induced and human-induced causes of desertification. Because of a large variance of climate conditions and human impact in the study region during the last two decades, changes in vegetation cover are difficult to link with only degradation process. They can reflect climate trends over the time or changes in land use. A new analytic methodology for the detection of areas undergoing degradation process driven by different reasons, climatic or anthropogenic, was developed and checked out in this project. The conceptual framework for the analysis is the combined use of Normalized Difference Vegetation Index (NDVI) time-series and precipitation time series over a 20-year period. Linear regression was used to determine trends in NDVI and precipitation for each pixel. First, changes in vegetation activity imposed by climate change were identified and pixels whose negative trends in NDVI are associated with downwards trends in precipitation considered to indicate climate-induced desertification. Areas with negative trends in NDVI which were not explained by trends in precipitation considered to experience human-induced degradation. Results were validated by test of statistical significance and by comparison with the data from the remote sensing systems of fine resolution and trips to some field sites. These modeling results were then combined with land-cover information to provide an assessment of desertification status.

Keywords: Land degradation; NDVI; Rainfall; Trend analysis; Regression modeling