Development of monitoring method of coffee leaf rust fungus (Hemileia vastatrix) infected area using satellite remote sensing

Since 2008, coffee leaf rust fungus (Hemileia vastatrix) has expanded its infection in Latin America, and early trimming and burning infected trees have been only effective countermeasures to prevent spreading infection. Although some researchers reported a case about the monitoring of coffee leaf rust using satellite remote sensing in 1970s, the spatial resolution was unsatisfied, and therefore, further technological development has been required. The purpose of this research is to develop effective method of discovering coffee leaf rust infected areas using satellite remote sensing. Annual changes of vegetation indices, i.e. Normalized Difference Vegetation Index (NDVI) and Modified Structure Insensitive Pigment Index (MSIPI), around Cuchumatanes Mountains, Republic of Guatemala, were analyzed by Landsat 7 images. Study fields in the research were limited by the coffee farm areas based on a previous paper about on site surveys in different damage areas. As the result of the analysis, the annual change of NDVI at the coffee farm areas with damages tended to be lower than those without damages. Moreover, the decline of NDVI appear from 2008 before the damage was reported. On the other hand, the change of MSIPI had no significant difference. NDVI and MSIPI are mainly related to the amount of chlorophyll and carotenoid in the leaves respectively. This means that the infected coffee leaves turned yellow without defoliation. This situation well matches the symptom of coffee leaf rust. The research concluded that the property of infected leaves turning yellow is effective to monitoring of infection areas by satellite remote sensing.