Using Full and Partial Unmixing Algorithms to Estimate the Inundation Extent of Small, Isolated Stock Ponds in an Arid Landscape

Authors: Christopher J Jarchow; Brent H Sigafus; Erin Muths; Blake R Hossack
Contribution Number: 698

https://link.springer.com/article/10.1007/s13157-019-01201-7#citeas

Abstract/Summary

Many natural wetlands around the world have disappeared or been replaced, resulting in the dependence of many wildlife species on small, artificial earthen stock ponds. These ponds provide critical wildlife habitat, such that the accurate detection of water and assessment of inundation extent is required. We applied a full (linear spectral mixture analysis; LSMA) and partial (matched filtering; MF) spectral unmixing algorithm to a 2007 Landsat 5 and a 2014 Landsat 8 satellite image to determine the ability of a time-intensive (i.e., more spectral input; LSMA) vs. a more efficient (less spectral input; MF) spectral unmixing approach to detect and estimate surface water area of stock ponds in southern Arizona, USA and northern Sonora, Mexico. Spearman rank correlations (rs) between modeled and actual inundation areas less than a single Landsat pixel (< 900 m2) were low for both techniques (rs range = 0.22 to 0.62), but improved for inundation areas > 900 m2 (rs range = 0.34 to 0.70). Our results demonstrate that the MF approach can model ranked inundation extent of known pond locations with results comparable to or better than LSMA, but further refinement is required for estimating absolute inundation areas and mapping wetlands < 1 Landsat pixel.

Publication details
Published Date: 2019-08
Outlet/Publisher: Wetlands
Media Format:

ARMI Organizational Units:
Rocky Mountains, Northern - Biology
Southwest, Arizona - Biology
Topics:
Drought; Quantitative Developments; Water
Place Names:
Arizona; Mexico; Sonoran Desert
Keywords:
climate; critical habitat; drought; geospatial models; habitat; hydroperiod; land cover/land use; methods; pond-breeding amphibians; recovery; surface water; T&E; water; wetlands
Notice: PDF documents require Adobe Reader or Google Chrome Browser (recommended) for viewing.