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Wetlands in the state of Arizona

Dates

Publication Date
Start Date
2019-01-01
End Date
2019-12-31

Citation

Soulard, C.E., Walker, J.J., Smith, B.W., and Kreitler, Jason R., 2023, Wetlands in the state of Arizona: U.S. Geological Survey data release, https://doi.org/10.5066/P9BC3WKD.

Summary

We created a single map of surface water presence by intersecting water classes from available land cover products (National Wetland Inventory, Gap Analysis Program, National Land Cover Database, and Dynamic Surface Water Extent) across the U.S. state of Arizona. We derived classified samples for four wetland classes from the harmonized map: water, herbaceous wetlands, wooded wetlands, and non-wetland cover. In Google Earth Engine (GEE) we developed a random forest model that combined the training data with spatially explicit predictor variables of vegetation greenness indices, wetness indices, seasonal index variation, topographic variables, and hydrologic parameters. The final product is a wall-to-wall map of general wetland types [...]

Contacts

Point of Contact :
Christopher E Soulard
Process Contact :
Christopher E Soulard
Originator :
Christopher E Soulard, Jessica J Walker, Britt W Smith, Jason R Kreitler
Metadata Contact :
Christopher E Soulard
Publisher :
U.S. Geological Survey
Distributor :
U.S. Geological Survey - ScienceBase
SDC Data Owner :
Western Geographic Science Center
USGS Mission Area :
Ecosystems

Attached Files

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Arizona_Wetlands_2019_4_class.tif.xml
Original FGDC Metadata
View 		20.18 KB application/fgdc+xml
Extension: Arizona_Wetlands_2019_4class.zip
Arizona_Wetlands_2019_4class.tif thumbnail 14.7 MB
Arizona_Wetlands_2019_4class.tif-ColorRamp.SLD 2.07 KB

Purpose

Wetland mapping efforts in the United States are particularly scarce in Arizona, where wetlands represent a small part of the landscape yet provide a wide range of ecosystem services. In this study we tested the feasibility of expediting the classification process by sourcing requisite training and testing data from existing national-scale land cover maps instead of customized sample sets, and using the data to train a machine learning (random forest) classification.

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Provenance

Level 1 revision initiated on September 24, 2024 by Christopher E Soulard. The overall accuracy in the abstract is old (84.8%) and was changed to 86.2% to match the paper.

Additional Information

Identifiers

Type Scheme Key
DOI https://www.sciencebase.gov/vocab/category/item/identifier doi:10.5066/P9BC3WKD

Raster Extension

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Raster Extension

nameArizona_Wetlands_2019_4class
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rasterTypeGeoTIFF

Raster Extension

files
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dateUploadedMon Jun 24 08:31:14 MDT 2024
nameArizona_Wetlands_2019_4class
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rasterTypeGeoTIFF

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