Ensemble Neural Networks for Modeling DEM Error

Simple item page
dc.contributor.authorNguyen, Chuyen
dc.contributor.authorStarek, Michael J.
dc.contributor.authorTissot, Philippe E.
dc.contributor.authorCai, Xiaopeng
dc.contributor.authorGibeaut, James C.
dc.date.accessioned2021-06-02T19:23:58Z
dc.date.available2021-06-02T19:23:58Z
dc.date.issued2019-10-09
dc.description.abstractDigital 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.en_US
dc.identifier.citationNguyen C, Starek MJ, Tissot PE, Cai X, Gibeaut J. Ensemble Neural Networks for Modeling DEM Error. ISPRS International Journal of Geo-Information. 2019; 8(10):444. https://doi.org/10.3390/ijgi8100444en_US
dc.identifier.doihttps://doi.org/10.3390/ijgi8100444
dc.identifier.urihttps://hdl.handle.net/1969.6/89669
dc.publisherMultidisciplinary Digital Publishing Instituteen_US
dc.subjectDEMen_US
dc.subjectUncertaintyen_US
dc.subjectTerrestrial Laser Scanningen_US
dc.subjectLidaren_US
dc.subjectEnsemble neural networks (ENNs)en_US
dc.subjectWetlanden_US
dc.titleEnsemble Neural Networks for Modeling DEM Erroren_US
dc.typeArticleen_US

Files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
Ensemble Neural Networks for Modeling DEM Error.pdf
Size:
8.26 MB
Format:
Adobe Portable Document Format
Description:
Full text
Download

License bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.72 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

publications