High-resolution landcover classification using Random Forest

Abstract

Potential data sets for landcover classification, such as Landsat (or pre-processed data such as the National Land Cover Dataset (NLCD)), are often too coarse for fine-scale research needs or are cost-prohibitive (Quickbird, Ikonos and Geoeye). Repeated attempts at classifying high spatial resolution data, National Agricultural Imagery Program (NAIP) imagery, based on traditional techniques, such as a maximum likelihood supervised classification, have failed to produce a product with sufficient accuracy. We used the ensemble Random Forests (RFs) classifier to classify landcover at 1 m resolution using 2009 NAIP imagery in south-eastern Wyoming. We classified riparian areas within a 225 km² study area, at 1 m spatial resolution, using RFs with emphasis on riparian corridors that yielded a land cover map with overall accuracy of 81% and a kappa coefficient of 79%. Users’ accuracy of important riparian vegetation species, aspen, riparian grasses and willow were 79%, 81% and 83%, respectively. Techniques presented in this paper, which exploit free NAIP imagery for landcover classification, represent an inexpensive and reliable alternative to purchasing commercial imagery when high spatial resolution landcover data is needed.

Acknowledgements

The authors would like to thank Shannon Albeke (Wyoming Geographic Information Science Center) and Ned Horning (Biodiversity Informatics Facility) for help with Random Forests and R implementation. We would also like to thank the anonymous reviewers who helped to improve this manuscript. Use of commercial names is for information purposes only and does not signify endorsement by the authors or their institutions.

Funding

This work was supported by the Wyoming Game & Fish Department under a cooperative research agreement with the University of Wyoming [grant number WYGF40452].