USING OPEN DATA CUBE ON THE CLOUD TO INVESTIGATE FOOD SECURITY BY MEANS OF CROPLAND CHANGES IN DJIBOUTI
Keywords: Open Data Cube, Food Security, Cropland, Hyperspectral Imaging, Machine Learning
Abstract. Addressing hunger is one of the greatest unsolved challenges Humanity has ever faced. Africa is one of the most fragile ecosystems strongly affected by numerous factors ranging from climate change, increasing population, decreasing water resources, and undeveloped hydrological infrastructure. These factors make it exceptionally vulnerable to food insecurity. The purpose of this study was to establish the feasibility and methodology of using Open Data Cube (ODC) and conventional machine learning algorithms to determine the extent of decrease in cropped area in the desert climate of Djibouti, the smallest Horn of Africa country (by landmass) over a thirty-year period. The research question was answered using Landsat 5, 7, and 8 imagery taken during the month of June from 1990, 2000, 2010, and 2020 then classified through machine learning algorithms - including decision tree and random forest. The data acquisition, analysis, and modeling were completed in an Open Data Cube environment using a cloud-based user computational platform running completely in-browser, and all necessary software was provided as part of the environment. The research identified a decreasing trend in vegetative areas but was limited in determining whether the vegetative areas were purely agricultural cropland in nature or included native vegetation. While the research reveals a concerning decline in total vegetation over the thirty-year period, the lack of other data variables (such as weather and climate patterns) provides too narrow a picture to determine causation. Several areas for further research are outlined.