Remote sensing monitoring of land restoration interventions in semi-arid environments using a before-after control-impact statistical design

Restoration interventions to combat desertification and land degradation are carried out in arid and semi-arid areas to improve vegetation cover and land productivity. Evaluating the success of an intervention is challenging due various data constrains and the lack of standardized and affordable methodologies. We propose a semi-automatic methodology to provide a first, standardised and objective assessment of the biophysical impact, in terms of vegetation cover, of restoration interventions using remote sensing data. The normalized difference vegetation index (NDVI) is used as a proxy of vegetation cover. Recognizing that changes in the environment are natural (e.g. due to the seasonal vegetation development cycle and the inter-annual climate variability), conclusions about the success of the intervention cannot be drawn by focussing on the intervention area only. We thus use a comparative method that analyses the temporal (before/after the intervention) variations of the NDVI of the impacted area with respect to multiple control sites that are automatically selected. The method provides an estimate of the magnitude of the differential change of the intervention area and the statistical significance of the no-change hypothesis test. Controls are randomly drawn from a set of candidates that are similar to the intervention area. As an example, the methodology is applied to restoration interventions carried out within the framework of the Great Green Wall for the Sahara and the Sahel Initiative in Senegal. The impact of the interventions is analysed using data at two different resolutions: 250 m of the Moderate Resolution Imaging Spectroradiometer and 30 m of the Landsat mission.