Spatio-temporal analyses of cropland degradation in the irrigated lowlands of Uzbekistan using remote-sensing and logistic regression modeling

Olena Dubovyk,Gunter Menz,Miriam Machwitz,Christopher Conrad,Asia Khamzina,Elena Kan

doi:10.1007/s10661-012-2904-6

Abstract

Advancing land degradation in the irrigated areas of Central Asia hinders sustainable development of this predominantly agricultural region. To support decisions on mitigating cropland degradation, this study combines linear trend analysis and spatial logistic regression modeling to expose a land degradation trend in the Khorezm region, Uzbekistan, and to analyze the causes. Time series of the 250-m MODIS NDVI, summed over the growing seasons of 2000–2010, were used to derive areas with an apparent negative vegetation trend; this was interpreted as an indicator of land degradation. About one third (161,000 ha) of the region’s area experienced negative trends of different magnitude. The vegetation decline was particularly evident on the low-fertility lands bordering on the natural sandy desert, suggesting that these areas should be prioritized in mitigation planning. The results of logistic modeling indicate that the spatial pattern of the observed trend is mainly associated with the level of the groundwater table (odds = 330 %), land-use intensity (odds = 103 %), low soil quality (odds = 49 %), slope (odds = 29 %), and salinity of the groundwater (odds = 26 %). Areas, threatened by land degradation, were mapped by fitting the estimated model parameters to available data. The elaborated approach, combining remote-sensing and GIS, can form the basis for developing a common tool for monitoring land degradation trends in irrigated croplands of Central Asia.

Highlights

In the Central Asian countries of the Aral Sea Basin (ASB), about 22 million people depend on irrigated agriculture, while the downstream countries Uzbekistan and Turkmenistan use about 80 % of the water from the ASB (2008)
The analyses were performed in two stages: (1) land degradation (LD) mapping based on the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time series, and (2) spatial logistic regression modeling
The MODIS data were found suitable for regionalscale monitoring of negative vegetation trends, which can be interpreted in relation to LD

Summary

Introduction

In the Central Asian countries of the Aral Sea Basin (ASB), about 22 million people depend on irrigated agriculture, while the downstream countries Uzbekistan and Turkmenistan use about 80 % of the water from the ASB (2008). The result is the Aral Sea shrinkage due to irrigation water withdrawal from the tributary rivers, rising groundwater tables and subsequent soil salinization (Spoor and Krutov 2003). The land degradation (LD) due to salinity has plagued about 75 % of the irrigated area of the ASB (van Dijk et al 1999), leading to reduced productivity of the arable land and, eventually, its withdrawal from agriculture. The annual losses in Uzbekistan due to LD were estimated at US$ 31 million, while withdrawal of highly salinized land out of agricultural production cost US$ 12 million (World Bank 2002). The demarcation of the irrigated cropland areas affected by LD, and identification of direct and proximate causes of LD would make it possible to target areas for mitigation efforts and to prioritize those in need of immediate policy attention

Methods

Results

Discussion

Conclusion