Abstract
Monitoring changes in the distribution and density of plant species often requires accurate and high-resolution baseline maps of those species. Detecting such change at the landscape scale is often problematic, particularly in remote areas. We examine a new technique to improve accuracy and objectivity in mapping vegetation, combining species distribution modelling and satellite image classification on a remote sub-Antarctic island. In this study, we combine spectral data from very high resolution WorldView-2 satellite imagery and terrain variables from a high resolution digital elevation model to improve mapping accuracy, in both pixel- and object-based classifications. Random forest classification was used to explore the effectiveness of these approaches on mapping the distribution of the critically endangered cushion plant Azorella macquariensis Orchard (Apiaceae) on sub-Antarctic Macquarie Island. Both pixel- and object-based classifications of the distribution of Azorella achieved very high overall validation accuracies (91.6–96.3%, κ = 0.849–0.924). Both two-class and three-class classifications were able to accurately and consistently identify the areas where Azorella was absent, indicating that these maps provide a suitable baseline for monitoring expected change in the distribution of the cushion plants. Detecting such change is critical given the threats this species is currently facing under altering environmental conditions. The method presented here has applications to monitoring a range of species, particularly in remote and isolated environments.
Highlights
There is increasing interest in monitoring landscape-scale changes in the distributions of plants caused by impacts including climate change, species invasions, and management actions [1,2]
We examine the capacity of random forests (RF) for mapping a sub-Antarctic cushion plant species using pixel- and objectbased classification of environmental and spectral variables
Random forest classifications based on a combination of terrain and spectral input variables accurately predicted the presence of Azorella with very high accuracy, regardless of which classification was used (κ = 0.848–0.924; Figure 3)
Summary
There is increasing interest in monitoring landscape-scale changes in the distributions of plants caused by impacts including climate change, species invasions, and management actions [1,2]. Monitoring changes in the distribution of individual species or communities often requires the creation of accurate high-resolution maps. Such maps can be used to monitor responses to environmental changes at regional or landscape scales, and complement plot-level studies. The production of these maps is time-consuming and expensive, and extensive research has been directed at improving mapping methods [3,4]. Incorporating environmental variables into satellite image classification (e.g. [6,8,13]) has the potential to improve the classification by pairing structural and disturbance information from the satellite imagery with the potential habitat information for individual species from species distribution modelling
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
