Using open-source software and digital imagery to efficiently and objectively quantify cover density of an invasive...

The most commonly used method for measuring vegetation cover is visual estimation, which is highly subjective, potentially leading to measurement errors. This poses serious implications to the assessment and continued management of plant species cover, for example in the control of invasive plant species. Morphological analysis of digital imagery has, to date, been primarily applied in the classification of landscape features. Our novel application of morphological image analysis provides an objective method for detection and accurate cover assessment of an invasive alien plant species (IAS), giving reduced measurement errors when compared to visual estimation. Importantly, this method is entirely based on free software. Guidos Toolbox is a collection of generic raster image processing routines, including Morphological Spatial Pattern Analysis (MSPA), which classifies and quantifies features according to shape. MSPA was employed in this study to detect and quantify cover of invasive Petasites pyrenaicus (Winter heliotrope) in digital images of 1 m × 1 m plots. Its efficacy was compared to that of two other methods- GIS Digitisation (used as an accurate baseline) and Visual Estimation (standard method). We tested the limit of MSPA usability on images of varying complexity, i.e. "simple", intermediate" or "complex", depending on presence/absence of other vascular plant species and the species richness of plot. Our results show good agreement between all three methods. MSPA measurement of P. pyrenaicus cover was most closely aligned with the GIS Digitisation (concordance correlation coefficients of 0.966). Visual Estimation was less closely aligned with GIS Digitisation (concordance correlation coefficients of 0.888). However, image complexity resulted in differing levels of agreement; with the closest agreement being achieved between MSPA and GIS Digitisation when used on images of lower and higher complexity. MSPA consistently provides higher accuracy and precision for P. pyrenaicus cover measurement than the standard Visual Estimation method. Our methodology is applicable to a range of focal vegetation species, both herbaceous and graminoid. Future application of MSPA for larger-scale surveying and monitoring via remote sensing is discussed, potentially reducing resource demands and increasing cover measurement consistency and accuracy. We recommend this method forms part of vegetation management toolkits for not only environmental managers, but for anyone concerned with plant cover assessment, from agricultural systems to sustainable resource use.