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

Model code and software

Wetland Intrinsic Potential (WIP) Tool D. Miller, M. Halabisky, D. Lorigan, and T. Brasel

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.