Proxy global assessment of land degradation

TLDR

Land degradation is a persistent issue, yet its causes, extent, and severity remain contested. Land degradation is defined as a long‑term decline in ecosystem function and productivity, quantified by long‑term remotely sensed NDVI and adjusted for rainfall effects using rain‑use efficiency, allowing deviations from the norm to proxy degradation and improvement. The 23‑year GIMMS NDVI analysis reveals a decline in rain‑use efficiency‑adjusted NDVI, indicating that 24 % of global land—mainly in sub‑equatorial Africa, Southeast Asia, southern China, north‑central Australia, the Pampas, and parts of Siberian and North American taiga—has degraded, affecting 1.5 billion people, and contradicting earlier assessments that conflated present and historical degradation.

Abstract

Abstract 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.