Laure Boudinaud

Mostly earth-observing West Africa; within WFP, applying geospatial techniques to the humanitarian sector and exploring linkages between conflict and land cover changes.

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Assessing cropland changes from violent conflict in central Mali with Sentinel-2 and Google Earth Engine
Alex Orenstein, Laure Boudinaud

In Central Mali, climate change, food insecurity and growing conflicts over land use necessitate being able to localize areas of food production (Benjaminsen, 2018) . The region’s heavy reliance on subsistence agriculture livelihoods means that humanitarian actors must quickly assess changes in cropland to plan the distribution of food aid. Typically, in the absence of extensive field data, publicly available land cover datasets are used to identify cropland cover. While the proliferation of such datasets (e.g. ESA-CCI or GlobeLand30) has increased over the years, they are often ill-adjusted to the Sahelian context. Assessments conducted of cropland identified by the most used land cover datasets found that none were able to meet the 75% accuracy threshold in Sahelian West Africa (Samasse et al, 2019). While countries like Mali are among those most critically in need of cropland mapping, the current toolkit of landcover data is woefully inadequate for the needs of humanitarian actors.
To address this gap, the “3-Period TimeScan” (3PTS) was developed using Google Earth Engine (Gorelick et al., 2017). This product consists of a Red-Green-Blue composite of Sentinel-2 Images where the red band represents the maximum NDVI value during the first period of the growing season, the green the maximum NDVI in the middle, and the blue the maximum NDVI at the end. This condensation of the agricultural season’s temporal evolution singles out cropland from other landcover types. A highly localized cropland change analysis was conducted comparing the 2019 3PTS product with the one of 2017, a year prior to the start of the Central Mali’s conflict. The change status was visually determined per populated site, as supervised classifications required exhaustive manual cleaning to produce a reliable product over such a large and ecologically heterogenous zone. The resulting map was compared with georeferenced data of conflict events, indicating a strong spatial correlation between violence and cropland reductions.