Mapping and monitoring riparian vegetation distribution, structure and composition with regression tree models and post-classification change metrics

Abstract

Riparian systems have become increasingly susceptible to both natural and human disturbances as cumulative pressures from changing land use and climate alter the hydrological regimes. This article introduces a landscape dynamics monitoring protocol that incorporates riparian structural classes into the land-cover classification scheme and examines riparian change within the context of surrounding land-cover change. We tested whether Landsat Thematic Mapper (TM) imagery could be used to document a riparian tree die-off through the classification of multi-date Landsat images using classification and regression tree (CART) models trained with physiognomic vegetation data. We developed a post-classification change map and used patch metrics to examine the magnitude and trajectories of riparian class change relative to mapped disturbance parameters. Results show that catchments where riparian change occurred can be identified from land-cover change maps; however, the main change resulting from the die-off disturbance was compositional rather than structural, making accurate post-classification change detection difficult.

Acknowledgements

This research was partially funded by the National Park Service Sonoran Desert Network and Sonoran Institute. The authors thank Dr A. Hubbard, Coordinator of the NPS I&M Sonoran Desert Network, and C. McIntyre of the Sonoran Institute for support and interest in developing a landscape dynamics monitoring protocol for Sonoran Desert parks. The authors thank Dr S. Drake, Dr A. McCoy, M. Dahl, the Friends of Santa Cruz River, The Environmental Protection Agency and Santa Cruz County for providing validation data at the alliance level as a result of their 2006 vegetation mapping efforts along the Upper Santa Cruz River Valley. The authors also thank Dr S. Yool for guidance during project development and for valuable comments on the manuscript, and the Arizona Remote Sensing Center, particularly Dr S. Marsh, for facilitating and supporting this research.