Preprints
https://doi.org/10.5194/egusphere-2022-665
https://doi.org/10.5194/egusphere-2022-665
 
11 Oct 2022
11 Oct 2022
Status: this preprint is open for discussion.

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

Meghan Halabisky1, Dan Miller2, Anthony J. Stewart1, Daniel Lorigan2, Tate Brasel2, and L. Monika Moskal1 Meghan Halabisky et al.
  • 1School of Environmental and Forest Sciences, University of Washington, Seattle, WA, USA
  • 2Terrainworks, Seattle, WA, USA;

Abstract. Accurate, un-biased 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 detect directly in aerial and satellite imagery. Wetlands that are obscured by forest canopy, occur ephemerally, and those without visible standing water are, therefore, often missing from wetland maps. To aid in detection of these cryptic wetlands, we developed the Wetland Intrinsic Potential tool, based on a wetland indicator 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, the WIP tool can identify areas conducive to wetland formation and provides a flexible approach that can be adapted to diverse landscapes. For a study area in the Hoh River Basin 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 Wetland Inventory, the classified WIP-tool output increased areas classified as wetland by 160 % 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 using a combination of R and python scripts in ArcGIS.

Meghan Halabisky et al.

Status: open (until 06 Dec 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Meghan Halabisky et al.

Data sets

WIP training and validation data and input datasets Meghan Halabisky, Dan Miller, Anthony Stewart https://terrainworks.sharefile.com/d-s88888b342d3f49d09b067683a73a916e

Model code and software

WIP tool code Dan Miller , Meghan Halabisky , Anthony J. Stewart , Daniel Lorigan , Tate Brasel https://github.com/TerrainWorks-Seattle/ForestedWetlands

Meghan Halabisky et al.

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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 aerial and satellite imagery. We developed a machine learning approach using spatially mapped variables of wetland indicators (i.e., vegetation, hydrology, soils) to predict wetland probability across a landscape. Our tool provides a flexible approach that can be adapted to diverse landscapes to improve wetland detection.