FCE LTER Journal Articles
Title
Object-based correction of LiDAR DEMs using RTK-GPS data and machine learning modeling in the coastal Everglades
Abstract
Light Detection and Ranging (LiDAR) Digital Elevation Models (DEMs) are frequently applied in modeling coastal environments. We present an object-based correction approach for accurate and precise DEMs by integrating LiDAR point data, aerial imagery, and Real Time Kinematic-Global Positioning Systems. Four machine learning techniques (Random Forest, Support Vector Machine, k-Nearest Neighbor, and Artificial Neural Network) were compared with the commonly used bias-correction method. The Random Forest object-based model produced best predictions for two study areas: Nine Mile (Mean Bias Error (MBE) reduced 0.18 to −0.02 m, Root Mean Square Error (RMSE) reduced 0.22 to 0.08 m) and Flamingo (MBE reduced 0.17 to 0.02 m, RMSE reduced 0.24 to 0.10 m). A Monte Carlo model was developed to combine errors into the object-based machine learning corrected DEMs, and uncertainty maps spatially revealed the likelihood of error. The object-based correction approach provides an attractive alternative to the bias-correction method.
Recommended Citation
Cooper, Hannah M.; Zhang, Caiyun; Davis, Stephen E.; and Troxler-Gann, Tiffany, "Object-based correction of LiDAR DEMs using RTK-GPS data and machine learning modeling in the coastal Everglades" (2018). FCE LTER Journal Articles. 490.
https://digitalcommons.fiu.edu/fce_lter_journal_articles/490
Rights Statement
In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/
This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).