Integrating multi-temporal spectral and structural information to map wetland vegetation in a lower Connecticut River tidal marsh

• Published in: Remote sensing of environment Vol. 112; no. 11; pp. 4048 - 4060
• Main Authors: Gilmore, Martha S., Wilson, Emily H., Barrett, Nels, Civco, Daniel L., Prisloe, Sandy, Hurd, James D., Chadwick, Cary
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 15.11.2008
• Subjects: Coastal wetlands; Image classification; LiDAR; Phragmites; Phragmites australis; QuickBird; Spartina patens; Spectroradiometer; Typha; Vegetation monitoring; USA, Connecticut R; Canada, British Columbia, Rock Creek; Vegetation monitoring; Phragmites australis; QuickBird; LiDAR; Coastal wetlands; Spectroradiometer; Image classification
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/j.rse.2008.05.020

This study examines the effectiveness of using multi-temporal satellite imagery, field spectral data, and LiDAR top of canopy data to classify and map the common plant communities of the Ragged Rock Creek marsh, located near the mouth of the Connecticut River. Visible to near-infrared (VNIR) reflectance spectra were measured in the field over the 2004–2006 growing seasons to assess the phenological variability of the dominant marsh plant species, Spartina patens, Phragmites australis and Typha spp. Phragmites was best distinguished from other species by its high NIR response late in the growing season. Typha spp. had a high red/green ratio and S. patens had a unique green/blue ratio relative to other species throughout the bulk of the growing season. The field spectra and single date (2004) LiDAR canopy height data were used to define an object-oriented classification methodology for the three plant communities in multi-temporal QuickBird multispectral imagery collected over the same time interval. The classification was validated using an extensive field inventory of marsh species. Overall maximum fuzzy accuracy for the classification was 97% for Phragmites, 63% for Typha spp. and 80% for S. patens meadows and improved to 97%, 76%, and 92%, respectively, using a fuzzy acceptable match measure. This study demonstrated the importance of the timing of image acquisition for the identification of targeted plant species in a heterogeneous marsh. These datasets and protocols may provide coastal resource managers, municipal officials and researchers a set of recommended guidelines for remote sensing data collection for marsh inventory and monitoring.