mproved coastal wetland mapping using very-high 2-meter spatial esolution imagery

Abstract Accurate wetland maps are a fundamental requirement for land use management and for wetland restoration planning. Several wetland map products are available today; most of them based on remote sensing images, but their different data sources and mapping methods lead to substantially different estimations of wetland location and extent. We used two very high-resolution (2 m) WorldView-2 satellite images and one (30 m) Landsat 8 Operational Land Imager (OLI) image to assess wetland coverage in two coastal areas of Tampa Bay (Florida): Fort De Soto State Park and Weedon Island Preserve. An initial unsupervised classification derived from WorldView-2 was more accurate at identifying wetlands based on ground truth data collected in the field than the classification derived from Landsat 8 OLI (82% vs. 46% accuracy). The WorldView-2 data was then used to define the parameters of a simple and efficient decision tree with four nodes for a more exacting classification. The criteria for the decision tree were derived by extracting radiance spectra at 1500 separate pixels from the WorldView-2 data within field-validated regions. Results for both study areas showed high accuracy in both wetland (82% at Fort De Soto State Park, and 94% at Weedon Island Preserve) and non-wetland vegetation classes (90% and 83%, respectively). Historical, published land-use maps overestimate wetland surface cover by factors of 2–10 in the study areas. The proposed methods improve speed and efficiency of wetland map production, allow semi-annual monitoring through repeat satellite passes, and improve the accuracy and precision with which wetlands are identified.

[1]  M. Al-Hamdan,et al.  Using Remote Sensing Data to Evaluate Habitat Loss in the Mobile, Galveston, and Tampa Bay Watersheds , 2010 .

[2]  Palma Blonda,et al.  8-Band Image Data Processing of the Worldview-2 Satellite in a Wide Area of Applications , 2012 .

[3]  D. Lu,et al.  Extraction of urban impervious surfaces from an IKONOS image , 2009 .

[4]  T. Dahl,et al.  Status and Trends of Wetlands in the Coastal Watersheds of the Conterminous United States 2004 to 2009 , 2013 .

[5]  L. M. Cowardin,et al.  Classification of Wetlands and Deepwater Habitats of the United States , 2017 .

[6]  T. M. Lillesand,et al.  Remote Sensing and Image Interpretation , 1980 .

[7]  S. K. McFeeters.  The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features , 1996 .

[8]  E. Raabe,et al.  Tampa Bay Coastal Wetlands: Nineteenth to Twentieth Century Tidal Marsh-to-Mangrove Conversion , 2012, Estuaries and Coasts.

[9]  L. Dini,et al.  Hierarchical classification of complex landscape with VHR pan-sharpened satellite data and OBIA techniques , 2014 .

[10]  Joanne N. Halls,et al.  Habitat Mapping and Change Assessment of Coastal Environments: An Examination of WorldView-2, QuickBird, and IKONOS Satellite Imagery and Airborne LiDAR for Mapping Barrier Island Habitats , 2014, ISPRS Int. J. Geo Inf..

[11]  A. Evely.  Dead planet, living planet. Biodiversity and ecosystem restoration for sustainable development, a rapid response assessment. C. Nellemann, E. Corcoran (eds). 78: Birkland Trykkeri, Norway, 2010. ISBN 978‐82‐7701‐083‐0, 109pp. , 2012 .

[12]  J. Kovacs,et al.  Seasonal changes in leaf chlorophyll a content and morphology in a sub-tropical mangrove forest of the Mexican Pacific , 2012 .

[13]  Dengsheng Lu,et al.  Coastal wetland vegetation classification with a Landsat Thematic Mapper image , 2011 .

[14]  Antonio Wolf,et al.  Using WorldView 2 Vis-NIR MSI Imagery to Support Land Mapping and Feature Extraction Using Normalized Difference Index Ratios , 2011 .

[15]  R. Pu,et al.  A comparative analysis of high spatial resolution IKONOS and WorldView-2 imagery for mapping urban tree species , 2012 .

[16]  Ute Beyer,et al.  Remote Sensing And Image Interpretation , 2016 .

[17]  Alin Dobra,et al.  Decision Tree Classification , 2009, Encyclopedia of Database Systems.

[18]  K. Ruddick,et al.  Turbid wakes associated with offshore wind turbines observed with Landsat 8 , 2014 .

[19]  S. Landry,et al.  Prioritizing Habitat Restoration Goals in the Tampa Bay Watershed , 2012 .

[20]  T. Harris,et al.  Spectral target detection for detecting and characterizing floating marine debris , 2012 .

[21]  Ross S. Lunetta,et al.  Application of multi-temporal Landsat 5 TM imagery for wetland identification , 1999 .

[22]  Antonio F. Wolf,et al.  Using WorldView-2 Vis-NIR multispectral imagery to support land mapping and feature extraction using normalized difference index ratios , 2012, Defense + Commercial Sensing.

[23]  Stacy L. Ozesmi,et al.  Satellite remote sensing of wetlands , 2002, Wetlands Ecology and Management.

[24]  Le Wang,et al.  Distinguishing mangrove species with laboratory measurements of hyperspectral leaf reflectance , 2009 .

[25]  Julia A. Barsi,et al.  The next Landsat satellite: The Landsat Data Continuity Mission , 2012 .

[26]  Ram M. Narayanan,et al.  A review of wetlands remote sensing and defining new considerations , 2001 .