Land degradation is always with us but its causes, extent and severity are contested. We define land degradation as a long-term decline in ecosystem function and productivity, which may be assessed using long-term, remotely sensed normalized difference vegetation index (NDVI) data. Deviation from the norm may serve as a proxy assessment of land degradation and improvement -if other factors that may be responsible are taken into account. These other factors include rainfall effects which may be assessed by rain-use efficiency, calculated from NDVI and rainfall. Results from the analysis of the 23-year Global Inventory Modeling and Mapping Studies (GIMMS) NDVI data indicate declining rain-use efficiency-adjusted NDVI on ca. 24% of the global land area with degrading areas mainly in Africa south of the equator, South-East Asia and south China, north-central Australia, the Pampas and swaths of the Siberian and north American taiga; 1.5 billion people live in these areas. The results are very different from previous assessments which compounded what is happening now with historical land degradation. Economic appraisal can be undertaken when land degradation is expressed in terms of net primary productivity and the resultant data allow statistical comparison with other variables to reveal possible drivers.

Remotely sensed vegetation indices are widely used to detect greening and browning trends; especially the global coverage of time-series normalized difference vegetation index (NDVI) data which are available from 1981. Seasonality and serial auto-correlation in the data have previously been dealt with by integrating the data to annual values; as an alternative to reducing the temporal resolution, we apply harmonic analyses and non-parametric trend tests to the GIMMS NDVI dataset . Using the complete dataset, greening and browning trends were analyzed using a linear model corrected for seasonality by subtracting the seasonal component, and a seasonal non-parametric model. In a third approach, phenological shift and variation in length of growing season were accounted for by analyzing the time-series using vegetation development stages rather than calendar days. Results differed substantially between the models, even though the input data were the same. Prominent regional greening trends identified by several other studies were confirmed but the models were inconsistent in areas with weak trends. The linear model using data corrected for seasonality showed similar trend slopes to those described in previous work using linear models on yearly mean values. The non-parametric models demonstrated the significant influence of variations in phenology; accounting for these variations should yield more robust trend analyses and better understanding of vegetation trends.

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