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

ISPRS International Journal of Geo-Information

François Waldner³  Mohamed Abdallahi Babah Ebbe²  Keith Cressman¹  Pierre Defourny³ 

[1] Desert Locust Information Service, FAO/AGP, Rome 00153, Italy; E-Mail:;Centre National de Lutte Antiacridienne, Nouakchott BP665, Mauritania; E-Mail:;Earth and Life Institue—Environment, Université catholique de Louvain, Croix du Sud 2, Louvain-la-Neuve 1348, Belgium; E-Mail:

关键词: desert locust;    habitat mapping;    accuracy assessment;    resolution bias;   

DOI  :  10.3390/ijgi4042379

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.