Application of Machine Learning to the Prediction of Vegetation Health

Abstract

ABSTRACT:This project applies machine learning techniques to remotely sensed imagery to train and validate predictive models of vegetation health in Bangladesh and Sri Lanka. For both locations, we downloaded and processed eleven years of imagery from multiple MODIS datasets which were combined and transformed into two-dimensional matrices. We applied a gradient boosted machines model to the lagged dataset values to forecast future values of the Enhanced Vegetation Index (EVI). The predictive power of raw spectral data MODIS products were compared across time periods and land use categories. Our models have significantly more predictive power on held-out datasets than a baseline. Though the tool was built to increase capacity to monitor vegetation health in data scarce regions like South Asia, users may include ancillary spatiotemporal datasets relevant to their region of interest to increase predictive power and to facilitate interpretation of model results. The tool can automatically update predictions as new MODIS data is made available by NASA. The tool is particularly well-suited for decision makers interested in understanding and predicting vegetation health dynamics in countries in which environmental data is scarce and cloud cover is a significant concern.

In-Season Crop-Area Mapping for Wheat and Rice in Afghanistan and Bangladesh

“…Some of their approaches have been: unsupervised and supervised (Cheema and Bastiaanssen 2010;Konishi et al 2007;Lin 2012;Turner and Congalton 1998), rule-based (Boschetti et al 2017), phenology-based (Dong et al 2015(Dong et al , 2016, and time-series classification algorithms (Dong et al 2016;Shew and Ghosh 2019). Besides, MODIS too has been effectively used for rice mapping and monitoring application scales (Burchfield et al 2016;Nelson et al 2014;Shapla et al 2015). This is mainly due to high repetitiveness, the relatively small data size, and the high spectral resolution, and available bands which are particularly pertinent to agriculture (Whitcraft et al 2015;Zhang et al 2017).…”