The Concept of a Hydrophyte for Wetland Identification

BioScience ◽  
1991 ◽  
Vol 41 (4) ◽  
pp. 236-247 ◽  
Author(s):  
Ralph W. Tiner
Keyword(s):  
Wetland Identification

Deep learning Using Physically-Informed Input Data for Wetland Identification

2020 ◽  
Vol 126 ◽  
pp. 104665 ◽  
Author(s):  
Gina L. O'Neil ◽  
Jonathan L. Goodall ◽  
Madhur Behl ◽  
Linnea Saby
Keyword(s):  
Deep Learning ◽  
Input Data ◽  
Wetland Identification

An evaluation of DEMs derived from LiDAR and photogrammetry for wetland mapping

2008 ◽  
Vol 84 (6) ◽  
pp. 840-849 ◽  
Author(s):  
A R Hogg ◽  
J. Holland
Keyword(s):  
Accuracy Assessment ◽  
Light Detection And Ranging ◽  
Qualitative Assessment ◽  
Wetland Mapping ◽  
Aerial Photo ◽  
Mapping Accuracy ◽  
Digital Elevation ◽  
Wetland Identification ◽  
Lidar Dem ◽  
Topographic Relief

The Ontario Ministry of Natural Resources (OMNR) and Ducks Unlimited Canada (DUC) have been engaged in developing an efficient and accurate methodology for inventorying wetlands. Their progress in this area has demonstrated that Digital Elevation Models (DEMs) are crucial input for wetland identification and boundary delineation. The provincial DEM, however, has known precision limitations in areas of minimal topographic relief that cause considerable mapping error. This study explored whether wetland mapping derived from bare-earth light detection and ranging (LiDAR) data would overcome the limitations of the provincial DEM. An automated wetland mapping approach was applied to the 2 elevation datasets and the results were compared using 2 methods of validation. One hundred aerial-photo-interpreted sample plots were used to quantitatively measure the ability of each source to separate upland from wetland. An overlay of wetland maps created from the 2 DEM sources was then qualitatively assessed to further clarify the magnitude of discrepancy between the 2 mapping sources. The study concluded that LiDAR showed a significant improvement at p = 0.05 over the provincial DEM for mapping wetlands, improving overall mapping accuracy from 76% to 84%. However, an overlay analysis and qualitative assessment showed the magnitude of this reported improvement is greater than was quantified by the accuracy assessment and that an assessment scheme with different sample units may further elucidate this discrepancy. Key words: LiDAR, DEM, wetland, mapping

Effects of LiDAR DEM Smoothing and Conditioning Techniques on a Topography‐Based Wetland Identification Model

2019 ◽  
Vol 55 (5) ◽  
pp. 4343-4363 ◽  
Author(s):  
Gina L. O'Neil ◽  
Linnea Saby ◽  
Lawrence E. Band ◽  
Jonathan L. Goodall
Keyword(s):  
Wetland Identification ◽  
Lidar Dem ◽  
Identification Model
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