Image texture as a remotely sensed measure of vegetation structure

Abstract

Ecologists commonly collect data on vegetation structure, which is an important attribute for characterizing habitat. However, measuring vegetation structure across large areas is logistically difficult. Our goal was to evaluate the degree to which sample-point pixel values and image texture of remotely sensed data are associated with vegetation structure in a North American grassland–savanna–woodland mosaic. In the summers of 2008–2009 we collected vegetation structure measurements at 193 sample points from which we calculated foliage-height diversity and horizontal vegetation structure at Fort McCoy Military Installation, Wisconsin, USA. We also calculated sample-point pixel values and first- and second-order image texture measures, from two remotely sensed data sources: an infrared air photo (1-m resolution) and a Landsat TM satellite image (30-m resolution). We regressed foliage-height diversity against, and correlated horizontal vegetation structure with, sample-point pixel values and texture measures within and among habitats. Within grasslands, savanna, and woodland habitats, sample-point pixel values and image texture measures explained 26–60% of foliage-height diversity. Similarly, within habitats, sample-point pixel values and image texture measures were correlated with 40–70% of the variation of horizontal vegetation structure. Among habitats, the mean of the texture measure ‘second-order contrast’ from the air photo explained 79% of the variation in foliage-height diversity while ‘first-order variance’ from the air photo was correlated with 73% of horizontal vegetation structure. Our results suggest that sample-point pixel values and image texture measures calculated from remotely sensed data capture components of foliage-height diversity and horizontal vegetation structure within and among grassland, savanna, and woodland habitats. Vegetation structure, which is a key component of animal habitat, can thus be mapped using remotely sensed data.