Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 27, issue 20
Articles | Volume 27, issue 20
https://doi.org/10.5194/hess-27-3687-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-27-3687-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 27, issue 20
Research article
 | 
20 Oct 2023
Research article |  | 20 Oct 2023

The Wetland Intrinsic Potential tool: mapping wetland intrinsic potential through machine learning of multi-scale remote sensing proxies of wetland indicators

Meghan Halabisky, Dan Miller, Anthony J. Stewart, Amy Yahnke, Daniel Lorigan, Tate Brasel, and Ludmila Monika Moskal

Viewed

Total article views: 2,592 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,557 985 50 2,592 54 46
  • HTML: 1,557
  • PDF: 985
  • XML: 50
  • Total: 2,592
  • BibTeX: 54
  • EndNote: 46
Views and downloads (calculated since 11 Oct 2022)
Cumulative views and downloads (calculated since 11 Oct 2022)

Viewed (geographical distribution)

Total article views: 2,592 (including HTML, PDF, and XML) Thereof 2,557 with geography defined and 35 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 08 May 2024
Download
Short summary
Accurate wetland inventories are critical to monitor and protect wetlands. However, in many areas a large proportion of wetlands are unmapped because they are hard to detect in imagery. We developed a machine learning approach using spatially mapped variables of wetland indicators (i.e., vegetation, hydrology, soils), including novel multi-scale topographic indicators, to predict wetland probability. Our approach can be adapted to diverse landscapes to improve wetland detection.
Read more