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

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

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...

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Bibliographic Details
Main Authors: Aaron E. Maxwell, Michael P. Strager, Timothy A. Warner, Christopher A. Ramezan, Alice N. Morgan, Cameron E. Pauley
Format: Article
Language:English
Published: MDPI AG 2019-06-01
Series:Remote Sensing
Subjects:
land cover
land cover mapping
object-based image analysis
GEOBIA
machine learning
random forests
National Agriculture Imagery Program
NAIP
Online Access:https://www.mdpi.com/2072-4292/11/12/1409

Internet

https://www.mdpi.com/2072-4292/11/12/1409

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