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

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Latest update: 23 Nov 2024
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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.
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