Operational Monitoring of the Desert Locust Habitat with Earth Observation: An Assessment

Abstract

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 ISPRS Int. J. Geo-Inf. 2015, 4 2380 to landscape fragmentation (R 2 = 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.

Cropland Mapping over Sahelian and Sudanian Agrosystems: A Knowledge-Based Approach Using PROBA-V Time Series at 100-m

“…In addition, part of the noise is related to the spectral temporal features themselves. Spectral-temporal features are based on extreme values and are thus more sensitive to noise, as noise itself is characterized by extreme values [66]. Region-dependent and dynamic cloud screening are currently being invested by the PROBA-Vegetation team and could significantly improve the cloud mask [75].…”

“…The accuracy assessment described in Section 3.4 provides a global performance evaluation, although it is well established that classification accuracy varies across space and that errors are not equally distributed spatially [59,[64][65][66]. Eight potential explanatory variables were proposed to explain the classification accuracy computed by OA and the F-score: latitude and longitude of the grid cell center, availability of cloud-free data and five landscape metrics indices.…”

A Dynamic Vegetation Senescence Indicator for Near-Real-Time Desert Locust Habitat Monitoring with MODIS

“…The performance of the six classification (three algorithms and two sets of metrics) was assessed by means of the error matrix and derived statistics, such as: the overall accuracy (OA), the Kappa coefficient (κ), the omission and commission errors (OE and CE) and the F 1 -score. In the framework of desert locust habitat monitoring, [32] showed that accuracy varies from one region to another and also along seasons within the same region. Therefore, the spatial, temporal and thematic variability of the classification accuracy has also been analyzed: (1) the RAMSES points were gathered by macro-region (i.e., homogeneous ecological units defined by [2]), and the global classification error (1 − OA) by macro-region has been represented spatially; (2) the RAMSES points were grouped by month, and the global error for each month was computed; and (3) the classification errors were calculated for each three vegetation density categories of the RAMSES database ("low" (25 points), "middle" (625 points), "dense" (368 points)).…”

“…To ensure consistency with the green area product, the method was only applied to pixels flagged as vegetation by the green area, as only vegetation can dry. One of the strengths of the dynamic greenness maps of the green area was its color code, allowing users to rapidly interpret the vegetation status and relate it to locust development [32]. Similarly, a color code was established for the dry season thanks to a simple time meter accumulating the detections for 1, 2, 3 and 4 or more 10-day intervals of each class.…”

“…Furthermore, commission errors were reduced thanks to the MODIS SWIR bands. Since 2010, dynamic greenness maps (i.e., the green area, developed by [30]) are provided every ten days to the Food and Agriculture Organization and the affected countries to help them analyze the current situation and plan the control operations [30][31][32]. These maps highlight vegetated areas close to the onset of greenness, which correspond to green and fresh vegetation that is highly palatable to desert locust.…”