Desert locust swarms intermittently damage crops and pastures in sixty countries from Africa to western Asia, threatening the food security of 10% of the world’s population. During the 20th century, desert locust control operations began organizing, and nowadays, they are coordinated by the Food and Agriculture Organization (FAO), which promotes a preventative strategy based on early warning and rapid response. This strategy implies a constant monitoring of the populations and of the ecological conditions favorable to their development. Satellite remote sensing can provide a near real-time monitoring of these conditions at the continental scale. Thus, the desert locust control community needs a reliable detection of green vegetation in arid and semi-arid areas as an indicator of potential desert locust habitat. To meet this need, a colorimetric transformation has been developed on both SPOT-VEGETATION and MODIS data to produce dynamic greenness maps. After their integration in the daily locust control activities, this research aimed at assessing those dynamic greenness maps from the producers’ and the users’ points of view. Eight confusion matrices and Pareto boundaries were derived from high resolution reference maps representative of the temporal and spatial diversity of Mauritanian habitats. The dynamic greenness maps were found to be accurate in summer breeding areas (F-score = 0.64–0.87), but accuracy dropped in winter breeding areas (F-score = 0.28–0.40). Accuracy is related to landscape fragmentation (R2 = 0.9): the current spatial resolution remains too coarse to resolve complex fragmented patterns and accounts for a substantial (60%) part of the error. The exploitation of PROBA-V 100-m images at the finest resolution (100-m) would enhance by 20% the vegetation detection in fragmented habitat. A survey revealed that end-users are satisfied with the product and find it fit for monitoring, thanks to an intuitive interpretation, leading to more efficiency. Full article

Desert locusts (Schistocerca gregaria) represent a major threat for agro-pastoral resources and food security over almost 30 million km2 from northern Africa to the Arabian peninsula and India. Given the differential food preferences of this insect pest and the extent and remoteness of the their distribution area, near-real-time remotely-sensed information on potential habitats support control operations by narrowing down field surveys to areas favorable for their development and prone to gregarization and outbreaks. The development of dynamic greenness maps, which detect the onset of photosynthetic vegetation, allowed national control centers to identify potential habitats to survey, as locusts prefer green and fresh vegetation. Their successful integration into the daily control operations led to a new need: the near-real-time identification of the onset of dryness, a synonym for the loss of habitat attractiveness, likely to be abandoned by locusts. The timely availability of this information would enable control centers to focus their surveys on areas more prone to gregarization, leading to more efficiency in the allocation of resources and in decision making. In this context, this work developed an original method to detect in near-real-time the onset of vegetation senescence. The design of the detection relies on the temporal behavior of two indices: the Normalized Difference Vegetation Index, depending on the green vegetation, and the Normalized Difference Tillage Index, sensitive to both green and dry vegetation. The method is demonstrated in Mauritania, an ever-affected country, with 10-day MODIS mean composites for the years 2010 and 2011. The discrimination performance of three classes (“growth”, “density reduction” and “drying”) were analyzed for three classification methods: maximum likelihood (61.4% of overall accuracy), decision tree (71.5%) and support vector machine (72.3%). The classification accuracy is heterogeneous in both time and space and is affected by several factors, such as vegetation density, the north-south climatic gradient and the relief. Smoothing the vegetation time series resulted in an increase of the overall accuracy of about 5% at the expense of a loss in timeliness of ten days. To simulate near-real-time monitoring conditions, the decision tree was applied to the decade of 2010. Overall, the seasonal vegetation cycle appeared clear and consistent. The results obtained pave the way for an operational implementation of the senescence dynamic mapping and, consequently, to further strengthen the capacity of the locust control management. Full article