Mapping rice field anopheline breeding habitats in Mali, West Africa, using Landsat ETM+ sensor data

• Published in: International journal of remote sensing Vol. 25; no. 2; pp. 359 - 376
• Main Authors: Diuk-Wasser, M. A., Bagayoko, M., Sogoba, N., Dolo, G., TourÉ, M. B., TraorÉ, S. F., Taylor, C. E.
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis Group 01.01.2004; Taylor and Francis
• Subjects: Biological and medical sciences; Fundamental and applied biological sciences. Psychology; Medically important nuisances and vectors, pests of stored products and materials: population survey and control; Vectors. Intermediate hosts; Mali; Africa; Cartography; Protozoal disease; Malaria; Insecta; Parasitosis; Breeding site; Remote sensing; Infection; Arthropoda; Culicidae; Paddy field; Ectoparasite; Invertebrata; Animal population; Vector; Diptera
• ISSN: 0143-1161; 1366-5901
• DOI: 10.1080/01431160310001598944

The aim of this study was to determine whether remotely sensed data could be used to identify rice-related malaria vector breeding habitats in an irrigated rice growing area near Niono, Mali. Early stages of rice growth show peak larval production, but Landsat sensor data are often obstructed by clouds during the early part of the cropping cycle (rainy season). In this study, we examined whether a classification based on two Landsat Enhanced Thematic Mapper (ETM)+ scenes acquired in the middle of the season and at harvesting times could be used to map different land uses and rice planted at different times (cohorts), and to infer which rice growth stages were present earlier in the season. We performed a maximum likelihood supervised classification and evaluated the robustness of the classifications with the transformed divergence separability index, the kappa coefficient and confusion matrices. Rice was distinguished from other land uses with 98% accuracy and rice cohorts were discriminated with 84% accuracy (three classes) or 94% (two classes). Our study showed that optical remote sensing can reliably identify potential malaria mosquito breeding habitats from space. In the future, these 'crop landscape maps' could be used to investigate the relationship between cultivation practices and malaria transmission.