Integrated LiDAR and IKONOS multispectral imagery for mapping mangrove distribution and physical properties

Abstract

The distribution of mangroves and other tropical and subtropical vegetation in the Greater Everglades Ecosystem is largely dependent on subtle variations in elevation, with mangroves occupying the lowest elevations. Combining a digital terrain model (DTM) derived from last-return light detection and ranging (LiDAR) data with IKONOS multispectral imagery in a maximum likelihood supervised classification resulted in a 7.1% increase in overall classification accuracy among seven classes (red mangrove, black mangrove, tropical hardwood hammock, coastal rock barren vegetation, mudflat, sand/rock and asphalt) compared with using the multispectral imagery alone, and the classification accuracy was improved for all four spectrally similar vegetation classes. A digital canopy model (DCM) was created by subtracting the digital terrain model from a digital surface model derived from LiDAR first returns. The DCM-recorded heights well correlated with mangrove canopy heights measured in the field but were systematically lower, by up to 2 m, for the tallest canopy. LiDAR has been documented to underestimate vegetation heights but the presence of water beneath some of the red mangrove canopy probably exacerbated this effect. The DCM and empirical allometric algorithms were used to estimate stem density and biomass for the classified red and black mangroves.

Acknowledgements

The author thanks Andrew Fricker of Airborne-1 Corporation for assistance with preprocessing of the LiDAR data, Jon Fajans and the boat crews of the Florida Keys Marine Laboratory on Long Key for their assistance with fieldwork, Claire Chadwick for invaluable assistance in the field and the staff of Long Key State Park for permission to conduct ground truth activities in the park. The author also thanks Mark Danson of the International Journal of Remote Sensing and two anonymous reviewers for helpful and valuable comments that improved the manuscript. This research was funded in part by the University of North Carolina at Charlotte.