Geostatistical models with the use of hyperspectral data and seasonal variation - A new approach for evaluating the risk posed by invasive plants

The general trend of ongoing plant invasion and the increasing number of species that may become invasive in the future, force seeking solutions that can improve the efficiency and economy of their management. Thus, we applied a novel approach combining the use of geostatistical interpolators such as ordinary kriging (OK) and cokriging (CK)-with environmental and hyperspectral data to evaluate the potential threat associated with the distribution of invasive plant species and to predict their probabilities of occurrence above the selected threshold of 10%. The specific spatial patterns of the probability of occurrence of Heradentn sosnowskyi and Fallopia spp. were modelled in two study areas in southern Poland. The significant achievement of this study was the application of geostatistical tools producing results characterized by a degree of precision quantified by cross-validation errors, and prediction errors after field verification. OK and CK returned root mean squared error (RMSE) values in a range from 0.21 to 0.51 and 0.21 to 0.47, respectively. For OK and CK, the prediction errors resulting from field verification in the following year were between 0.03-0.39, and 0.03 0.29, respectively. Additionally, the study provided the first prediction maps (2D) and Digital Prediction Models (DPMs) (3D) visualizations of the probability of occurrence of both invasive plants. Although the proposed approach is illustrated with real case studies related to Heracleum sostwwskyi and Pallopia spp., it could be extended to other species. This demonstrates the potential of an effective alternative strategy for evaluating the risk posed by invasive plants, that will be able to provide fast, low cost and effective prediction and monitoring of their spread. For institutions dealing with invasive plants, this may be beneficial and help to reduce the negative consequences of their improper management.