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

Villarreal, Miguel Luis, Van Leeuwen, Willem J. D., Romo-Leon, Jose Raul
International journal of remote sensing 2012 v.33 no.13 pp. 4266-4290
Landsat, climate, die-off, humans, hydrology, image analysis, land cover, land use, models, monitoring, regression analysis, trees, vegetation, vegetation structure, watersheds
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.