Using multi-date satellite imagery to monitor invasive grass species distribution in post-wildfire landscapes: An iterative, adaptable approach that employs open-source data and software

• Published in: International journal of applied earth observation and geoinformation 2017-07, Vol.59, p.135-146
• Main Authors: West, Amanda M., Evangelista, Paul H., Jarnevich, Catherine S., Kumar, Sunil, Swallow, Aaron, Luizza, Matthew W., Chignell, Stephen M.
• Format: Article
• Language: English
• Subjects: Bromus tectorum; computer software; data collection; geographical distribution; habitats; invasive species; Land management; Landsat; landscapes; Random Forests; remote sensing; risk; seed dispersal; Software for Assisted Habitat Modeling; space and time; topography; wildfires
• ISSN: 1569-8432; 1872-826X
• DOI: 10.1016/j.jag.2017.03.009

[Display omitted] •Open-source data and software provide robust tools for monitoring invasive species.•An iterative approach and threshold for species cover greatly improves detection.•Multi-date Landsat imagery is appropriate for distinguishing high grass cover. Among the most pressing concerns of land managers in post-wildfire landscapes are the establishment and spread of invasive species. Land managers need accurate maps of invasive species cover for targeted management post-disturbance that are easily transferable across space and time. In this study, we sought to develop an iterative, replicable methodology based on limited invasive species occurrence data, freely available remotely sensed data, and open source software to predict the distribution of Bromus tectorum (cheatgrass) in a post-wildfire landscape. We developed four species distribution models using eight spectral indices derived from five months of Landsat 8 Operational Land Imager (OLI) data in 2014. These months corresponded to both cheatgrass growing period and time of field data collection in the study area. The four models were improved using an iterative approach in which a threshold for cover was established, and all models had high sensitivity values when tested on an independent dataset. We also quantified the area at highest risk for invasion in future seasons given 2014 distribution, topographic covariates, and seed dispersal limitations. These models demonstrate the effectiveness of using derived multi-date spectral indices as proxies for species occurrence on the landscape, the importance of selecting thresholds for invasive species cover to evaluate ecological risk in species distribution models, and the applicability of Landsat 8 OLI and the Software for Assisted Habitat Modeling for targeted invasive species management.