Mapping changes of grassland to arable land using automatic machine learning of stacked ensembles and H2O library

Abstract

Permanent grasslands play a very important role in the landscape. The loss of permanent grasslands and their subsequent conversion into arable land create erosion-prone agricultural areas in the landscape and have a negative impact on the biodiversity. From this point of view, there is a need for the accurate and effective monitoring of changes in the agricultural landscape along with an assessment of the influence of the agricultural policies on the landscape. Sentinel-2 from the Copernicus programme has improved options for the implementation of remote sensing data into the monitoring of agricultural land. The aim of this study was to evaluate the potential of H2O library and within implemented Automachine learning function (AutoML) and its stacked ensembles for mapping changes from grasslands to arable lands. All results show high overall accuracy from 93.5% to 96.6% and high values of area under the ROC curve (0.94–0.98). Stacked ensembles appear to be the most accurate machine learning models for mapping changes from grasslands to arable lands. The importance of several biological predictors has been tested (FAPAR, FCOVER, LAI, NDVI, etc.) with the help of a heatmap that is part of AutoML function of H2O library.

Keywords

• Stacked ensembles
• machine learning
• H2O library
• grassland mapping
• change detection