Using Landsat Time Series to Understand How Management and Disturbances Influence the Expansion of an Invasive Tree

Abstract

Invasive plant management is both challenging and expensive and as such strategic and well-informed decisions may contribute for a more effective management. Remote sensing time series can inform decision-making, improving management strategies. Acacia longifolia is one of the most widespread invasive plants in Portuguese coastal areas. We used this species to test a new approach to assess how different management practices and disturbances may influence invasive plants’ distribution. The Mann–Kendall statistical test was applied to a 15-year time series (2000–2015) of Landsat TM/ETM+ derived normalized difference vegetation index to detect statistically significant vegetation trends. These maps were interpreted together with a land-cover map derived from a combined rule-based and supervised classification of a Landsat OLI image from November 27th 2013; this interpretation was then complemented with information about management practices and disturbances allowing to identify the processes influencing the current distribution of A. longifolia . The overall accuracy of the classification was 0.785 (Kappa 0.753) and A. longifolia was detected in 9% (8691 ha) of the study area. Of the nine processes of land cover change identified, “vegetation cover intensification” and “vegetation recovery after removal” were the main drivers of expansion. Pine forests were vulnerable to invasion but offered resistance to conversion into A. longifolia monospecific stands, while herbaceous and shrub habitats seemed less resistant to invasion. Some interventions aiming to control A. longifolia were shown to aggravate the invasion. This approach facilitates monitoring the invasion and allows managers to allocate resources to areas where management may be most effective.