View source for Halabisky et al. 2023 wetland intrinsic potential tool

{{Product
|Type Group=Document
|Type Document=No Subtype
|Authors Document=Halabisky et al.
|Year Document=2023
|Title Document=wetland intrinsic potential tool
|FileOrCitation Document=No File
|Jurisdictions=Washington
|AnthroTopic=Water Management;Conservation Lands
|EcoTopic=Wetland
|Effort=Research
}}
'''Halabisky, M., Miller, D., Stewart, A. J., Yahnke, A., Lorigan, D., Brasel, T., & Moskal, L. M. (2023). The wetland intrinsic potential tool: mapping wetland intrinsic potential through machine learning of multi-scale remote sensing proxies of wetland indicators. ''Hydrology and Earth System Sciences'', ''27''(20), 3687-3699.'''

*https://hess.copernicus.org/articles/27/3687/2023/hess-27-3687-2023.pdf

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=== Abstract ===
Accurate, unbiased wetland inventories are critical to monitor and protect wetlands from future harm or land conversion. However, most wetland inventories are constructed through manual image interpretation or automated classification of multi-band imagery and are biased towards wetlands that are easy to directly detect in aerial and satellite imagery. Wetlands that are obscured by forest canopy, that occur ephemerally, and that have no visible standing water are, therefore, often missing from wetland maps. To aid in the detection of these cryptic wetlands, we developed the Wetland Intrinsic Potential (WIP) tool, based on a wetlandindicator framework commonly used on the ground to detect wetlands through the presence of hydrophytic vegetation, hydrology, and hydric soils. Our tool uses a random forest model with spatially explicit input variables that represent all three wetland indicators, including novel multi-scale topographic indicators that represent the processes that drive wetland formation, to derive a map of wetland probability. With the ability to include multi-scale topographic indicators that help identify cryptic wetlands, the WIP tool can identify areas conducive to wetland formation while providing a flexible approach that can be adapted to diverse landscapes. For a study area in the Hoh River watershed in western Washington, USA, classification of the output probability with a threshold of 0.5 provided an overall accuracy of 91.97%. Compared to the National Wetlands Inventory, the classified WIP tool output identified over 2 times the wetland area and reduced errors of omission from 47.5% to 14.1% but increased errors of commission from 1.9% to 10.5%. The WIP tool is implemented as an ArcGIS toolbox using a combination of R and Python scripts.