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

Acknowledgments

We would like to thank to the the European Union’s Caroline Herschel Framework. We would like to thank the anonymous reviewers for their substantial help with improving the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Author contributions

Jiří Šandera conceptualised and suggested the methodology, programmed the software, executed the main investigation and wrote the original draft. Přemysl Štych contributed and supervised the methods and text and provided the main funding. All the authors have read and agreed to the published version of the manuscript.

Supplemental data

Supplemental data for this article can be accessed online at https://doi.org/10.1080/22797254.2023.2294127

Additional information

Funding

This study was supported by the European Union’s Caroline Herschel Framework Partnership Agreement on Copernicus User Uptake under grant agreement no. FPA 275/G/GRO/COPE/17/10042, project FPCUP (Framework Partnership Agreement on Copernicus User Uptake).