Volume XLI-B2
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B2, 465-469, 2016
https://doi.org/10.5194/isprs-archives-XLI-B2-465-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B2, 465-469, 2016
https://doi.org/10.5194/isprs-archives-XLI-B2-465-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  08 Jun 2016

08 Jun 2016

APPLICATION OF MACHINE LEARNING TO THE PREDICTION OF VEGETATION HEALTH

Emily Burchfield1, John J. Nay2, and Jonathan Gilligan3 Emily Burchfield et al.
  • 1Dept. of Civil and Environmental Engineering, Vanderbilt University, Nashville, TN 37235 USA
  • 2School of Engineering, Vanderbilt University, Nashville, TN 37235 USA
  • 3Dept. of Earth and Environmental Science, Vanderbilt University, Nashville, TN 37235, USA

Keywords: Machine learning, predictive modeling, decision support, open source software, vegetation index

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.