Application of Probabilistic Machine Learning to the prediction of Remotely Sensed Vegetation Health

Ahmed T. Hammad 

Every prediction of the future carries some level of uncertainty; making this explicit is challenging. This paper introduces a Probabilistic Machine Learning algorithm, namely the Natural Gradient Boosting algorithm, as a modelling tool for predicting the Vegetation Health Index, a proxy for monitoring vegetation stress in response to changing weather conditions. It then elaborates on the time heterogeneity of the probabilistic predictions generated by the model over one year on West Java, the most populated Indonesia region, providing important consideration for Food Security. The results highlight that while 94% of the observed values fall within the model’s 95% prediction interval, during February and March, the probability of reaching critically low levels of VHI below 40 is above 75%. The paper foster the multidisciplinary literature linking the use of remotely sensed vegetation health data with Food Security by incorporating uncertainty in predicting the impact of changing weather conditions on Food Security.