Abstract
Understanding spatial distributions of invasive plant species at early infestation stages is critical for assessing the dynamics and underlying factors of invasions. Recent progress in very high resolution remote sensing is facilitating this task by providing high spatial detail over whole-site extents that are prohibitive to comprehensive ground surveys. This study assessed the opportunities and constraints to characterize landscape distribution of the invasive grass medusahead (Elymus caput-medusae) in a ∼36.8 ha grassland in California, United States from 0.15m-resolution visible/near-infrared aerial imagery at the stage of late spring phenological contrast with dominant grasses. We compared several object-based unsupervised, single-run supervised and hierarchical approaches to classify medusahead using spectral, textural, and contextual variables. Fuzzy accuracy assessment indicated that 44–100% of test medusahead samples were matched by its classified extents from different methods, while 63–83% of test samples classified as medusahead had this class as an acceptable candidate. Main sources of error included spectral similarity between medusahead and other green species and mixing of medusahead with other vegetation at variable densities. Adding texture attributes to spectral variables increased the accuracy of most classification methods, corroborating the informative value of local patterns under limited spectral data. The highest accuracy across different metrics was shown by the supervised single-run support vector machine with seven vegetation classes and Bayesian algorithms with three vegetation classes; however, their medusahead allocations showed some “spillover” effects due to misclassifications with other green vegetation. This issue was addressed by more complex hierarchical approaches, though their final accuracy did not exceed the best single-run methods. However, the comparison of classified medusahead extents with field segments of its patches overlapping with survey transects indicated that most methods tended to miss and/or over-estimate the length of the smallest patches and under-estimate the largest ones due to classification errors. Overall, the study outcomes support the potential of cost-effective, very high-resolution sensing for the site-scale detection of infestation hotspots that can be customized to plant phenological schedules. However, more accurate medusahead patch delineation in mixed-cover grasslands would benefit from testing hyperspectral data and using our study’s framework to inform and constrain the candidate vegetation classes in heterogeneous locations.
Highlights
Understanding spatial distribution of invasive plant species at the early stages of infestation is critical for exposing the drivers of their expansion and informing preventive management (Westbrooks, 2004; Mangla et al, 2011)
This study focused on a ∼36.8 ha grassland (“Campbell”) site (Figure 1) at the University of California’s Sierra Foothills Research Experimental Center (SFREC) in Yuba County, California, United States (39◦15.3 N, 121◦17.1 W)
Our results indicate that applying object-based image analysis (OBIA) framework to very high resolution VNIR imagery is a useful strategy for detecting general extents and hotspots of invasive medusahead infestations in the mixed-cover grasslands
Summary
Understanding spatial distribution of invasive plant species at the early stages of infestation is critical for exposing the drivers of their expansion and informing preventive management (Westbrooks, 2004; Mangla et al, 2011). The process of invasion is often non-linear in space and time (Coutts et al, 2011), and rapid transitions from smaller, scattered patches to larger monodominant areas pose significant challenges to their control (With, 2002; Regan et al, 2006) Such dynamics may depend on complex cross-scale ecological interactions and threshold behavior which affect broader-scale landscape composition, yet may be difficult to assess in their entirety (With, 2002; Mayer and Rietkerk, 2004; Peters et al, 2007; Suding and Hobbs, 2009). Several important challenges may affect the success of these efforts
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