资源环境科技发展态势分析平台

The dehesa ecosystem is severely affected by root rot decline caused by the soilborne pathogenic oomycetes of the genus Phytophthora. Defoliation mapping is particularly challenging in low density ecosystems with high spectral variability due to environmental heterogeneity within relatively short distances. In this study, holm oak defoliation was assessed by classifying two different levels (low and high defoliation trees, with a 30% crown-defoliation threshold) using a combination of multispectral WorldView-2 (WV-2) and Airborne Laser Scanning (ALS) data in an automated Random Forest (RF) modeling approach. Besides the original remote sensing data, a set of vegetation indices (VI) and ALS-derived structural parameters were derived. Seven predictors of defoliation were selected from the ALS metrics, WV-2 bands, and indices datasets: the height percentile of the tree in which occurs the maximum number of returns, mean values in the tree crown of the Chlorophyll index green, Plant Senescence Reflectance Index, Red Edge Normalized Difference Vegetation Index, Double Peak Index, band 3 of the WV-2 image, and bincentil 70 values. The confusion matrix of the external evaluation achieved the highest overall classification accuracy (86.7%) and the highest Kappa index (0.73). Finally, we produced a predicted tree-defoliation map which was used to establish the relationship between Quercus ilex damage levels and soil variables and management practices. High defoliated trees were more abundant in zones of shallow and compacted soil, with lower silt proportion and Ca concentration. Our results offer to forest managers a tool for the rapid and effective assessment of large areas affected by root rot defoliation in their planning of control and restoration measures aimed at reducing the holm oak mortality of holm oaks in management units.