"The African armyworm poses a significant threat to human food security in many regions. Detecting and monitoring the effects of this pest is, therefore, an activity that needs to be carried out with the utmost urgency. Field surveys in combination with remote sensing have the potential to play a pivotal role in understanding the distribution and effects of the African armyworm. However, finding remotely sensed data and reliable variables from field surveys to model and predict African armyworm distribution can be a daunting activity. Some of the challenges include: which vegetation index to use, how to manage cloud cover in satellite imagery and which spatial and temporal resolution to select. On the other hand, field surveys are not only subject to biased responses but a lack of recall power from the respondents. Despite these challenges, the onus falls on the research community to provide methods that can help address the uncertainties in these data sources for research on armyworm impacts on crops. This thesis consists of two coupled studies on armyworm infestation in Ejura, Ghana. The first is concerned with modeling the relationship between farmer-provided survey responses and vegetation quality as captured by satellite remote sensing. To assess the accuracy of field survey responses, the first study begins by hypothesizing that Enhanced Vegetation Index (EVI) has a positive correlation with armyworm infestation. This hypothesis was then tested through a logistic regression, where the dependent variable was farmers' declaration of presence or absence of armyworm infestation in 2017. Independent variables were principal components that measured slope and EVI from Landsat 8 for April, May, and July of 2017. Results from the logistic analysis revealed that there was no correlation between EVI, slope and armyworm infestation. Interestingly, a prediction model resulting from the logistic model performed well by correctly predicting 11 out of 13 armyworm infestation cases. Nevertheless, the model could only predict one case of absence of armyworm infestation out of five cases. The second study contrast two vegetation index products obtained at very different spatial resolutions. I envisage possible applications of the second finding from the second study in addressing the issue of cloud cover in satellite-based remote sensing by resampling fine-scale Parrot Sequoia imagery to Landsat 8 (30 m resolution) imagery. Although a time lag of 4 days was present between Landsat 8 imagery and data obtained from Parrot Sequoia multispectral camera deployed on a UAV, a prediction accuracy of 0.67 was achieved. Developing a technique that could rescale Parrot Sequoia data to Landsat 8 imagery is a novel aspect of this work. Fishnet, which is a popular tool in ArcMap, was instrumental in the rescaling phase of this study. Mapping residuals from the EVI (Landsat 8) and EVI2 (rescaled Parrot Sequoia) image showed that the regression model developed predicted well in areas with high and homogenous vegetation as compared to areas with low and heterogeneous vegetation."--Pages ii-ii.
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