the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
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
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.
Meghan Halabisky et al.
Status: final response (author comments only)
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RC1: 'Comment on egusphere-2022-665', Anonymous Referee #1, 12 Feb 2023
The manuscript presents a novel method called Wetland Intrinsic Potential, which based on a set of indicators selected by the authors aims to identify wetlands that are often missing from inventory due to presence of forest canopy, due to their ephemerality or because they have no visible standing water. The authors applied their method in a specific study area (Hoh River Basin), showing that they were able to increase the classification of wetland overall area with lower omission errors but higher commission errors.
As the authors mentioned in their introduction, wetland mapping is clearly one of the most interesting challenges for the wide array of functions of these ephemeral water bodies at landscape scale. For this reason, I commend the authors for their contribution in this research direction, however I am a little bit skeptical about the flexibility of their approach (that they mentioned both in the abstract and in the conclusion), and therefore the extension to other landscapes. I have listed below few suggestions and/or questions that might help the authors in improving their work.
The authors did a good job in the introduction listing several of the ongoing challenges and comparing them with accurate research. However, I suggest the authors to look at more recent manuscript that implement machine learning, random forest, or satellite images to extensively map wetlands both at higher and lower spatial resolution such as:
- Lane, C. R., D'Amico, E., Christensen, J. R., Golden, H. E., Wu, Q., and Rajib, A.: Mapping Global Non-Floodplain Wetlands, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-3, in review, 2023.
- Xiang et al., 2023. GWL_FCS30: a global 30 m wetland map with a fine classification system using multi-sourced and time-series remote sensing imagery in 2020
- Wu et al., 2019. Integrating LiDAR data and multi-temporal aerial imagery to map wetland inundation dynamics using Google Earth Engine
- Mullen, C., Bertassello, L. E., Rao, P. S. C., & Müller, M. F. (2022). From wetlands to wetlandscapes: Remote sensing calibration of process-based hydrological models in heterogeneous landscapes. Hydrological Processes, 36( 11), e14739. https://doi.org/10.1002/hyp.14739
In particular, it would be nice to compare the estimate of your model with other sources since the limitation of the NWI are quite well known. I believe that the recent data from Lane et al. 2023 are freely available and one of the datasets they use for validation is close to the region used by the authors. At least a comment on that would be appreciated.
Extension to other case studies. I feel this is a crucial point when it comes to wetland classification and preparation of new inventories as the authors stated at line 140. However, I am not sure how this can be done in places where training data (section 3.2) are not available. I would be curious to see – if possible – the application of the model in a different case study, or at least in the same case study without starting from a preliminary model and see how it compares with the current estimates.
A more technical question for the authors is about the threshold 0.5 they used for their binary classification. Can you comment a little bit more why they chose 0.5 and not a different number? Something that would be useful is a sensitivity analysis on the threshold value to strengthen the sentence at the end of section 5.1 “If users want to lower…selected”. I feel the sentence is a little bit weak and would need more quantitative estimates.
Minor comments:
Section 2.1. Can you add more specific of the case study? Such as the size of the watershed, the number of wetlands identified by the NWI?
Figure 1. Can you add the location of the case study also in the map in the inset just above the legend? Either a dot or the boundary of the watershed would be nice to have a sense on where to locate it for readers that are not familiar with the region.
Table 1. I am not sure I understand what some of the numbers in the table are. Is 85 the number of identified wetlands? The percentage? Please improve the caption of the table so a reader can understand what is going on.
Citation: https://doi.org/10.5194/egusphere-2022-665-RC1 -
RC2: 'Comment on egusphere-2022-665', Anonymous Referee #2, 27 Feb 2023
The authors developed a wetland mapping tool for identifying wetlands through machine learning algorithms (e.g., random forest) applied to remote sensing datasets. Three wetland indicators are considered in the proposed framework, including hydrophytic vegetation, hydrology, and hydric soils. NAIP imagery, Lidar data, and SSURGO data correspond to each wetland indicator, respectively. The proposed framework was applied to mapping potential wetlands in the Hoh River Basin in Washington, USA. The results show that the proposed framework reduced the omission error, but increased the commission error when compared to the National Wetlands Inventory (NWI).
Mapping wetlands is a challenging task in remote sensing. This wetland mapping tool developed by the authors can potentially be useful to the wetland mapping community. See below my comments for the authors’ consideration:
- There is a Wetland Identification Model (WIM) that has been available through Arc Hydro since 2020. The WIM methodology is similar to the proposed method in this manuscript, except that WIM only considers DEM data. This manuscript also considers vegetation and soil data. However, these two wetland indicators have also been widely studied in the literature. What’s new in the proposed method compared to what has been available in the literature? The O'Neil et. al (2018) has been cited in this manuscript, but the O'Neil et. al 2019 and 2020 papers on the WIM models are not. Why not build upon WIM rather than starting from scratch?
- Can the authors make the resulting data products (overlaid on NWI layers) available to the public? Maybe through ArcGIS Online and an Earth Engine App so that readers can visually compare the authors’ wetland mappings to NWI. Although the commission and omission errors seem reasonable, I am more interested in how the resulting products align with NWI at a fine scale. I am always a bit skeptical about new wetland products unless I can visualize them on an interactive map and compare them with well-known wetland products such as the NWI.
- The proposed wetland tool produced an increased wetland area by 160% compared to NWI. Why? This needs an in-depth discussion. As a reader, I am interested in knowing when the tool works best, and when it fails.
- What is the minimum mapping unit used in this study? Did the authors do any post-processing to reduce the salt-and-pepper effect of the resulting wetland maps? How would that affect the omission and commission error calculations?
- In terms of the accuracy assessments, did the authors perform both pixel-based and object-based accuracy assessments?
- The data used in this study are mostly available at the national scale. For example, NAIP and SSURGO data are available at the national scale, and LiDAR data are also available for the majority of the US through the USGS 3DEP program. The training data are derived from NWI, which is also available nationally. I would hope that the proposed tool can be applied to other areas. However, the authors stated in Section 5.2 that their intention was not to develop a model that could be extended to new areas without the collection of new training data. This greatly reduces the transferability of the method and usability of the tool.
- Lastly, here are two recently published papers on multi-scale geomorphometric analysis that might be of interest to the authors.
References:
- Wetland Identification Model: https://community.esri.com/t5/water-resources-blog/the-wetland-identification-model-wim-a-new-arc/ba-p/884298
- O'Neil, G. L., Goodall, J. L., Behl, M., Saby, L. (2020). Deep Learning using Physically-Informed Input Data for Wetland Identification. Environmental Modelling and Software. 104665. https://doi.org/10.1016/j.envsoft.2020.104665.
- O'Neil, G. L., Saby, L., Band, L. E., Goodall, J. L. (2019). Effects of LiDAR DEM Smoothing and Conditioning Techniques on a Topography-Based Wetland Identification Model. Water Resources Research, 55. https://doi.org/10.1029/2019WR024784.
- O'Neil, G. L., Goodall, J. L., Watson, L. T. (2018). Evaluating the potential for site-specific modification of LiDAR DEM derivatives to improve environmental planning-scale wetland identification using random forest classification. Journal of Hydrology, 559, 192-208.
- https://doi.org/10.1016/j.jhydrol.2018.02.009.
- Lindsay, J. B., Newman, D. R., & Francioni, A. (2019). Scale-optimized surface roughness for topographic analysis. Geosciences, 9(7), 322. https://doi.org/10.3390/geosciences9070322
- Newman, D. R., Cockburn, J. M., Drǎguţ, L., & Lindsay, J. B. (2022). Evaluating Scaling Frameworks for Multiscale Geomorphometric Analysis. Geomatics, 2(1), 36-51. https://doi.org/10.3390/geomatics2010003
Citation: https://doi.org/10.5194/egusphere-2022-665-RC2
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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