2021-09-29, 10:00–10:30, Academic
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.
In June 2019, during the planting period, a peak in both the numbers of violent events and of fatalities was recorded in central Mali. Most of the significant cropland losses occurred in localities where violent events were reported for the period between April and October 2019. Cropland abandonment, but also an effect of concentration of crops in the proximity of habitations (due to access restrictions, violent threats or attacks in farther fields) and settlement damage are as many consequences of the violence operating in central Mali, as visible from space.
The World Food Programme (WFP) operationalized the analysis from the methods detailed in this paper. By offering a map and a list of localities showing significant declines in cultivation, a more precise picture of food insecurity could be drawn, highlighting vulnerable areas in need of food assistance. These outputs were quickly absorbed into the humanitarian response planning process, notably through the Cadre Harmonisé (CH), the bi-annual national food security analysis (led by the national early warning system in collaboration with line ministries and humanitarian actors such as NGOs and UN agencies). The goal of the CH is to estimate the number of food insecure people in the country and provide coordinated targeting priorities for humanitarian response. The remote sensing results contributed to estimating 757,217 persons in food insecurity for the 2020 lean season (the seasonal period where hunger typically peaks during the year). Beyond the CH, the unprecedented level of spatial precision provided by these results fed into humanitarian response mechanisms and strategic decision-making, as a tool to enhance village-scale geotargeting of most vulnerable communities. WFP used these outputs to target their humanitarian assistance for the lean season of 2020 as early as March (2-3 months ahead of the start of the lean season).
Laure Boudinaud (1), Alex Orenstein (2)
(1) World Food Programme, Regional Bureau of West and Central Africa, Dakar (Senegal)
(2) DaCarte, Dakar (Senegal)
Alex is a cow mapper and drought specialist based in Senegal, focusing on using FOSS to better serve the needs of livestock herding communities as they adapt to climate change. More information can be found on his website (www.orensteingis.com) or twitter (@oren_sa).
Laure is mostly earth-observing Africa; within WFP, applying geospatial techniques to the humanitarian sector and exploring linkages between conflict and land cover changes (twitter @laure_boudi)Track –
2 - Basic. General basic knowledge is required.Language of the Presentation –
Alex Orenstein is a cow mapper and drought specialist based in Senegal. For most of the past decade, he has used FOSS to encourage open-data solutions to understand food insecurity in West Africa, with a focus on the needs of livestock herding communities. Most of his work has been on developing tools and methods to track the changing movements of livestock herds as they respond to climate change. More information can be found on his website (www.orensteingis.com) or twitter (@oren_sa).
Earth-observing Africa mostly!
Within WFP, applying geospatial techniques to the humanitarian sector and exploring linkages between conflict and land cover changes.