Predicting the co-invasion of two Asteraceae plant genera in post-mining landscapes using satellite remote sensing and airborne LiDAR.

ABSTRACT: The Asteraceae plant family includes the most widespread weedy invaders in Europe, which may jointly inhibit natural succession in degraded land under restoration. The complex local drivers of co-invasions hinder remote sensing (RS) monitoring efforts, as the links between the ecological and the spectral habitat properties are largely unknown. We proposed a comprehensive framework for machine learning modeling of the co-invasion of two Erigeron spp. and two Solidago spp. in post-mining landscapes of S Poland, using both field data and a combination of Sentinel-2, Landsat 7 and airborne LiDAR RS predictors. Stochastic Gradient Boosting best captured the non-linear dependencies (Accuracy = 0.670-0.886, AUC = 0.675-0.923), and generally outcompeted two other classifiers (Random Forest and Sup

SUBMITTER: Kedra K 

PROVIDER: S-EPMC12504456 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature