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Open AccessArticle

Large-Area, High Spatial Resolution Land Cover Mapping Using Random Forests, GEOBIA, and NAIP Orthophotography: Findings and Recommendations

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Department of Geology and Geography, West Virginia University, Morgantown, WV 26506, USA
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Davis College of Agriculture, Natural Resources, and Design, West Virginia University, Morgantown, WV 26506, USA
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Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(12), 1409; https://doi.org/10.3390/rs11121409
Received: 8 May 2019 / Revised: 8 June 2019 / Accepted: 11 June 2019 / Published: 13 June 2019
(This article belongs to the Special Issue Geographic Object-Based Image Analysis (GEOBIA))
Despite the need for quality land cover information, large-area, high spatial resolution land cover mapping has proven to be a difficult task for a variety of reasons including large data volumes, complexity of developing training and validation datasets, data availability, and heterogeneity in data and landscape conditions. We investigate the use of geographic object-based image analysis (GEOBIA), random forest (RF) machine learning, and National Agriculture Imagery Program (NAIP) orthophotography for mapping general land cover across the entire state of West Virginia, USA, an area of roughly 62,000 km2. We obtained an overall accuracy of 96.7% and a Kappa statistic of 0.886 using a combination of NAIP orthophotography and ancillary data. Despite the high overall classification accuracy, some classes were difficult to differentiate, as highlight by the low user’s and producer’s accuracies for the barren, impervious, and mixed developed classes. In contrast, forest, low vegetation, and water were generally mapped with accuracy. The inclusion of ancillary data and first- and second-order textural measures generally improved classification accuracy whereas band indices and object geometric measures were less valuable. Including super-object attributes improved the classification slightly; however, this increased the computational time and complexity. From the findings of this research and previous studies, recommendations are provided for mapping large spatial extents. View Full-Text
Keywords: land cover; land cover mapping; object-based image analysis; GEOBIA; machine learning; random forests; National Agriculture Imagery Program; NAIP land cover; land cover mapping; object-based image analysis; GEOBIA; machine learning; random forests; National Agriculture Imagery Program; NAIP
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MDPI and ACS Style

Maxwell, A.E.; Strager, M.P.; Warner, T.A.; Ramezan, C.A.; Morgan, A.N.; Pauley, C.E. Large-Area, High Spatial Resolution Land Cover Mapping Using Random Forests, GEOBIA, and NAIP Orthophotography: Findings and Recommendations. Remote Sens. 2019, 11, 1409.

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