Differentiating mine-reclaimed grasslands from spectrally similar land cover using terrain variables and object-based machine learning classification

Abstract

Incorporating ancillary, non-spectral data may improve the separability of land use/land cover classes. This study investigates the use of multi-temporal digital terrain data combined with aerial National Agriculture Imagery Program imagery for differentiating mine-reclaimed grasslands from non-mining grasslands across a broad region (6085 km²). The terrain data were derived from historical digital hypsography and a recent light detection and ranging data set. A geographic object-based image analysis (GEOBIA) approach, combined with two machine learning algorithms, Random Forests and Support Vector Machines, was used because these methods facilitate the use of ancillary data in classification. The results suggest that mine-reclaimed grasslands can be mapped accurately, with user’s and producer’s accuracies above 80%, due to a distinctive topographic signature in comparison with other spectrally similar grasslands within this landscape. The use of multi-temporal digital elevation model data and pre-mining terrain data only generally provided statistically significant increased classification accuracy in comparison with post-mining terrain data. Elevation change data were of value, and terrain shape variables generally improved the classification. GEOBIA and machine learning algorithms were useful in exploiting these non-spectral data, as data gridded at variable cell sizes can be summarized at the scale of image objects, allowing complex interactions between predictor variables to be characterized.

Acknowledgements

Lidar data were provided by the West Virginia Department of Environmental Protection (WVDEP) and the Natural Resource Analysis Center (NRAC) at West Virginia University. We would specifically like to acknowledge Adam Riley and Paul Kinder for their assistance in obtaining and processing the lidar data. We would like to thank two anonymous reviewers for their helpful comments that greatly improved the manuscript.

Additional information

Funding

Funding support for this study was provided by West Virginia View and the Appalachian College Association. The project described in this publication was also supported in part by grant number G14AP00002 from the Department of the Interior, United States Geological Survey to AmericaView. Its contents are solely the responsibility of the authors; the views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the US Government. Mention of trade names or commercial products does not constitute their endorsement by the US Government.