Sensitive wetlands delineation using multitemporal satellite imagery: a comparative study in the intermountain western U.S.

Bruce Cheney; Mark W. Jackson; Perry J. Hardin

doi:10.1117/12.413944

23 January 2001 Sensitive wetlands delineation using multitemporal satellite imagery: a comparative study in the intermountain western U.S.

Bruce Cheney, Mark W. Jackson, Perry J. Hardin

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Proceedings Volume 4171, Remote Sensing for Agriculture, Ecosystems, and Hydrology II; (2001) https://doi.org/10.1117/12.413944
Event: Europto Remote Sensing, 2000, Barcelona, Spain

Abstract

This paper details an effort to develop an operational methodology to distinguish lacustrine, palustrine and riverine wetlands from irrigated agriculture in a continental area using archived multi-temporal/multi-spectral Landsat TM data.. Archival Landsat TM data were acquired over the Little Wood River Valley of Idaho in April, August and September of 1985. All dates of imagery were subjected to a Kauth-Thomas transformation and then stacked into a single 9-band image and submitted to a supervised classification. DEM data was used to remove spectral confusion with mountain vegetative systems with similar temporal signatures to the wetlands of interest. Field checks and comparison to National Wetland Inventory (NWI) maps completed in 1984 revealed a 98.3% agreement in classification of non-wetland areas. 54% of the areas classified as wetland on the NWI were classified as wetland using our method. This is attributed to practice of generalization of the NWI maps in which several small wetlands are circumscribed into a single large area. The digital method correctly identified the wetland patches and classified the interstices as dry land. Confusion with irrigated agriculture was almost completely absent.

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Bruce Cheney, Mark W. Jackson, and Perry J. Hardin "Sensitive wetlands delineation using multitemporal satellite imagery: a comparative study in the intermountain western U.S.", Proc. SPIE 4171, Remote Sensing for Agriculture, Ecosystems, and Hydrology II, (23 January 2001); https://doi.org/10.1117/12.413944

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KEYWORDS

Satellites

Vegetation

Earth observing sensors

Satellite imaging

Image classification

Agriculture

Data modeling

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