Next Article in Journal
Remote Sensing Observation of Particulate Organic Carbon in the Pearl River Estuary
Next Article in Special Issue
Multiple Stable States and Catastrophic Shifts in Coastal Wetlands: Progress, Challenges, and Opportunities in Validating Theory Using Remote Sensing and Other Methods
Previous Article in Journal
Interpolation Routines Assessment in ALS-Derived Digital Elevation Models for Forestry Applications
Previous Article in Special Issue
Evaluation of Polarimetric SAR Decomposition for Classifying Wetland Vegetation Types
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2015, 7(7), 8655-8682; doi:10.3390/rs70708655

Development of a Bi-National Great Lakes Coastal Wetland and Land Use Map Using Three-Season PALSAR and Landsat Imagery

1
Michigan Tech Research Institute, Michigan Technological University, Ann Arbor, MI 48105, USA
2
Michigan State University Extension, Michigan Natural Features Inventory, Lansing, MI 48901, USA
3
McMaster University, Hamilton, ON L8S 4K1, Canada
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editors: Alisa L. Gallant and Prasad S. Thenkabail
Received: 9 February 2015 / Revised: 25 June 2015 / Accepted: 30 June 2015 / Published: 9 July 2015
(This article belongs to the Special Issue Towards Remote Long-Term Monitoring of Wetland Landscapes)
View Full-Text   |   Download PDF [7132 KB, uploaded 9 July 2015]   |  

Abstract

Methods using extensive field data and three-season Landsat TM and PALSAR imagery were developed to map wetland type and identify potential wetland stressors (i.e., adjacent land use) for the United States and Canadian Laurentian coastal Great Lakes. The mapped area included the coastline to 10 km inland to capture the region hydrologically connected to the Great Lakes. Maps were developed in cooperation with the overarching Great Lakes Consortium plan to provide a comprehensive regional baseline map suitable for coastal wetland assessment and management by agencies at the local, tribal, state, and federal levels. The goal was to provide not only land use and land cover (LULC) baseline data at moderate spatial resolution (20–30 m), but a repeatable methodology to monitor change into the future. The prime focus was on mapping wetland ecosystem types, such as emergent wetland and forested wetland, as well as to delineate wetland monocultures (Typha, Phragmites, Schoenoplectus) and differentiate peatlands (fens and bogs) from other wetland types. The overall accuracy for the coastal Great Lakes map of all five lake basins was 94%, with a range of 86% to 96% by individual lake basin (Huron, Ontario, Michigan, Erie and Superior). View Full-Text
Keywords: wetlands; synthetic aperture radar; PALSAR; Landsat; thermal; optical imagery; Typha; Phragmites; Schoenoplectus wetlands; synthetic aperture radar; PALSAR; Landsat; thermal; optical imagery; Typha; Phragmites; Schoenoplectus
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Bourgeau-Chavez, L.; Endres, S.; Battaglia, M.; Miller, M.E.; Banda, E.; Laubach, Z.; Higman, P.; Chow-Fraser, P.; Marcaccio, J. Development of a Bi-National Great Lakes Coastal Wetland and Land Use Map Using Three-Season PALSAR and Landsat Imagery. Remote Sens. 2015, 7, 8655-8682.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top