Preprints
https://doi.org/10.5194/egusphere-2022-665
https://doi.org/10.5194/egusphere-2022-665
11 Oct 2022
 | 11 Oct 2022

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, Daniel Lorigan, Tate Brasel, and L. Monika Moskal

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.

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Journal article(s) based on this preprint

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
Hydrol. Earth Syst. Sci., 27, 3687–3699, https://doi.org/10.5194/hess-27-3687-2023,https://doi.org/10.5194/hess-27-3687-2023, 2023
Short summary
Meghan Halabisky, Dan Miller, Anthony J. Stewart, Daniel Lorigan, Tate Brasel, and L. Monika Moskal

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-665', Anonymous Referee #1, 12 Feb 2023
    • AC1: 'Reply on RC1', Meghan Halabisky, 13 Apr 2023
  • RC2: 'Comment on egusphere-2022-665', Anonymous Referee #2, 27 Feb 2023
    • AC2: 'Reply on RC2', Meghan Halabisky, 14 Apr 2023

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-665', Anonymous Referee #1, 12 Feb 2023
    • AC1: 'Reply on RC1', Meghan Halabisky, 13 Apr 2023
  • RC2: 'Comment on egusphere-2022-665', Anonymous Referee #2, 27 Feb 2023
    • AC2: 'Reply on RC2', Meghan Halabisky, 14 Apr 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Reconsider after major revisions (further review by editor and referees) (17 Apr 2023) by Alberto Guadagnini
AR by Meghan Halabisky on behalf of the Authors (02 May 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (03 May 2023) by Alberto Guadagnini
RR by Anonymous Referee #2 (13 May 2023)
RR by Anonymous Referee #1 (27 May 2023)
ED: Publish subject to revisions (further review by editor and referees) (03 Jun 2023) by Alberto Guadagnini
AR by Meghan Halabisky on behalf of the Authors (16 Jun 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (22 Jun 2023) by Alberto Guadagnini
AR by Meghan Halabisky on behalf of the Authors (06 Sep 2023)

Journal article(s) based on this preprint

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
Hydrol. Earth Syst. Sci., 27, 3687–3699, https://doi.org/10.5194/hess-27-3687-2023,https://doi.org/10.5194/hess-27-3687-2023, 2023
Short summary
Meghan Halabisky, Dan Miller, Anthony J. Stewart, Daniel Lorigan, Tate Brasel, and L. Monika Moskal

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, Dan Miller, Anthony J. Stewart, Daniel Lorigan, Tate Brasel, and L. Monika Moskal

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Latest update: 04 Sep 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 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.