Developing A Satellite Based Automatic System For Crop Monitoring: Kenya's Great Rift Valley A Case Study
MetadataShow full item record
The crop growth stage represents essential information for agricultural areas management. In this study we investigate the feasibility of a tool based on remotely sensed satellite (Landsat 8) imagery, capable of automatically classify crop fields and how much resolution enhancement based on pan-sharpening techniques and phenological information extraction, useful to create decision rules that allow to identify semantic class to assign to an object, can effectively support the classification process. Moreover we investigate the opportunity to extract vegetation health status information from remotely sensed assessment of the equivalent water thickness (EWT). Our case study LVWKH.HQ\D¶V*UHDW5LIWYDOOH\ in this area a ground truth campaign was conducted during August 2015 in order to collect crop fields GPS measurements, leaf area index (LAI) and chlorophyll samples.
CitationLuciani R, Laneve G, Mito C, Jahjah M. "Developing a satellite based automatic system for crop monitoring: Kenya's Great Rift valley a case study.". In: ESA SP 740, LPS16, Proceedings of the conference held 9–13 May 2016. L. Ouwehand. Vol. 740.; 2016:.
RightsAttribution-NonCommercial-NoDerivs 3.0 United States
The following license files are associated with this item: