A novel technique using LiDAR to identify native-dominated and tame-dominated grasslands in Canada

• Published in: Remote sensing of environment Vol. 218; pp. 201 - 206
• Main Authors: Fisher, Ryan J., Sawa, Ben, Prieto, Beatriz
• Format: Journal Article
• Language: English
• Published: New York Elsevier Inc 01.12.2018; Elsevier BV
• Subjects: Agricultural equipment; Classification; Dominant species; Ecosystem assessment; Ecosystems; Exotic grassland; Forbs; Furrows; Grasses; Grassland; Grasslands; Image analysis; Image classification; Image processing; Image processing systems; Indigenous plants; Indigenous species; Introduced species; Land cover classification; LiDAR; Mapping; Native grassland; Remote sensing; Species classification; Tame grassland; Canada; Grassland; Tame grassland; Land cover classification; Native grassland; Exotic grassland; LiDAR
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/j.rse.2018.10.003

Native grassland in North America is considered one of the most imperiled and altered ecosystems. Unfortunately, an assessment of how much native grassland remains in North America is difficult because all nation-wide landcover mapping products do not reliably distinguish native grasslands from grasslands that have been deliberately planted with tame grasses and forbs (i.e., tame grasslands). We established a 218.5 km2 study area in southwestern Saskatchewan, Canada to evaluate the use of high-resolution Light Detection and Ranging (LiDAR) for classification of native (i.e., fields with native-dominant species mixes or fields that were formerly native species dominated but have been invaded by exotic species) and tame grasslands (i.e., grasslands dominated by exotic grass and forb species), and compared these classifications to the best-available landcover mapping product that is currently available for this area. We used the presence of tractor furrows, identified from the LiDAR digital terrain hillshade product, in tame-dominated grasslands to distinguish them from native-dominated grasslands that had an absence of tractor furrows. The LiDAR method achieved substantially better classification success (Cohen's Kappa = 0.57) at distinguishing native-dominated (N = 82) from tame-dominated grasslands (N = 45), than the currently available landcover product (Cohen's Kappa = 0.13) over our 218.5 km2 study area. Misclassification by LiDAR of fields that had been planted with tame grasses and forbs, but were starting to be re-established by native plants appeared to be one weakness of the method in the study area. Our research highlights a novel and time-efficient method for classifying LiDAR imagery using easily available image analysis features in ArcGIS. •Distinguishing native from tame grasslands is important for grassland conservation.•A new method using LiDAR was developed to identify these two landcover types.•We achieved better classification success than currently available landcover layers.•This technique is novel and time efficient when classifying large areas.