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Open AccessArticle

Ensemble Neural Networks for Modeling DEM Error

1
College of Science and Engineering, Texas A&M University-Corpus Christi, Corpus Christi, TX 78412, USA
2
School of Engineering and Computing Sciences, Conrad Blucher Institute for Surveying and Science, Texas A&M University-Corpus Christi, Corpus Christi, TX 78412, USA
3
Harte Research Institute, Texas A&M University-Corpus Christi, Corpus Christi, TX 78412, USA
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(10), 444; https://doi.org/10.3390/ijgi8100444
Received: 17 August 2019 / Revised: 29 September 2019 / Accepted: 2 October 2019 / Published: 9 October 2019
(This article belongs to the Special Issue Geospatial Monitoring with Hyperspatial Point Clouds)
Digital elevation models (DEMs) have become ubiquitous and remarkably effective in the field of earth sciences as a tool to characterize surface topography. All DEMs have a degree of inherent error and uncertainty that is propagated to subsequent models and analyses, which can lead to misinterpretation and inaccurate estimates. A new method was developed to estimate local DEM errors and implement corrections while quantifying the uncertainties of the implemented corrections. The method is based on the flexibility and ability to model complex problems with ensemble neural networks (ENNs). The method was developed to be applied to any DEM created from a corresponding set of elevation points (point cloud) and a set of ground truth measurements. The method was developed and tested using hyperspatial resolution terrestrial laser scanning (TLS) data (sub-centimeter point spacing) collected from a marsh site located along the southern portion of the Texas Gulf Coast, USA. ENNs improve the overall DEM accuracy in the study area by 68% for six model inputs and by 75% for 12 model inputs corresponding to root mean square errors (RMSEs) of 0.056 and 0.045 m, respectively. The 12-input model provides more accurate tolerance interval estimates, particularly for vegetated areas. The accuracy of the method is confirmed based on an independent data set. Although the method still underestimates the 95% tolerance interval, 8% below the 95% target, results show that it is able to quantify the spatial variability in uncertainties due to a relationship between vegetation/land cover and accuracy of the DEM for the study area. There are still opportunities and challenges in improving and confirming the applicability of this method for different study sites and data sets.
Keywords: DEM; uncertainty; terrestrial laser scanning; lidar; ensemble neural networks (ENNs); wetland DEM; uncertainty; terrestrial laser scanning; lidar; ensemble neural networks (ENNs); wetland
MDPI and ACS Style

Nguyen, C.; Starek, M.J.; Tissot, P.E.; Cai, X.; Gibeaut, J. Ensemble Neural Networks for Modeling DEM Error. ISPRS Int. J. Geo-Inf. 2019, 8, 444.

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