Object-based correction of LiDAR DEMs using RTK-GPS data and machine learning modeling in the coastal Everglades. (February 2019)

• Record Type: Journal Article
• Authors: Cooper, Hannah M.; Zhang, Caiyun; Davis, Stephen E.; Troxler, Tiffany G.
• Is Part Of: Environmental modelling & software. Volume 112(2019)
• Page Start: 179
• Page End: 191
• Publication Date: 2019-02
• Subjects: LiDAR -- Object-based image analysis -- Machine learning -- Monte Carlo -- DEMs -- Coastal wetlands; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2018.11.003
• Languages: English
• ISSNs: 1364-8152

Abstract

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. Highlights: Object-based correction reduces both bias and error when compared to bias-correction. Object-based correction addresses accuracy and error difference within a plant community. Object-based Random Forest model performed best in two separate study areas. Monte Carlo simulation combines bias and error into corrected DEMs for more reliable products.