Dealing with imperfect data for invasive species detection using multispectral imagery

Abstract

Detection and monitoring invasive species can provide valuable ecological information to guide management decisions. Multispectral imagery remote sensing may be an ideal tool to address this problem by providing accurate and affordable repeat imagery. However, developing training datasets for remote sensing imagery can be riddled with issues such as matching a training site to a single pixel in target imagery or mixed pixels caused by low invader densities. The multitarget multiple-instance spectral match filter (MTMI-SMF) algorithm accounts for such errors and has low computation costs. Here, we evaluated MTMI-SMF for invasive species detection with National Agriculture Imagery Program (NAIP), RapidEye, and Landsat imagery using Brazilian peppertree (Schinus terebinthifolia) in the Everglades National Park as a case study. MTMI-SMF detected Brazilian peppertree with an area under the curve (AUC) of 0.85 for Landsat, 0.82 for RapidEye, and 0.71 NAIP imagery. Invader classification was developed using a threshold calculated from MTMI-SMF image detection confidence values and compared to a random forest classification. MTMI-SMF and random forest classifications had similar overall accuracies with NAIP imagery performing the lowest (an average of 73.6% and 88.9%, respectively) and Landsat performing the highest (an average of 90.9% and 91.4%, respectively). However, due to the relatively rare nature of Brazilian peppertree across this landscape, the overall classification accuracy was not fully representative of detection capabilities. MTMI-SMF outperformed random forest for user accuracy and had comparable producer accuarcy across all three image sources. Classifications primarily relied on the brighter signature of Brazilian peppertree due to the limited number of spectral bands in the multispectral imagery. As a result, confusion was caused by heterogeneous vegetation communities or shrub habitats, which most likely could be resolved with the increased availability of satellite hyperspectral datasets. Our study indicates that MTMI-SMF, a machine-learning approach that allows flexibility in training data, can detect highly problematic invaders using multispectral imagery. This pipeline could be readily adapted to other invaders and ecosystems, as well as different remote sensing image sources, and has low computational requirements. Our study demonstrates that remote sensing technologies and multiple-instance learning algorithms can provide managers with critical tools for tackling the ever-growing, costly, and challenging problem of tracking invasive species' plant spread.