Mapping dead understorey Buxus hyrcana Pojark using Sentinel-2 and Sentinel-1 data

Abstract

The Hyrcanian Forests comprise a continuous 800-km belt of mostly deciduous broadleaf forests and are considered as Iran’s most important vegetation region in terms of density, canopy cover and species diversity. One of the few evergreen species of the Hyrcanian Forests is the box tree (Buxus), which is seriously threatened by box blight disease and box tree moth outbreaks. Therefore, information on the spatial distribution of intact and infested box trees is essential for recovery monitoring, control treatment and management. To address this critical knowledge gap, we integrated a genetic algorithm (GA) with a support vector machine (SVM) ensemble classification based on the combination of leaf-off optical Sentinel-2 and radar Sentinel-1 data to map the spatial distribution of box tree mortality. We additionally considered the overstorey species composition to account for a potential impact of overstory stand composition on the spectral signature of understorey defoliation. We consequently defined target classes based on the combination of dominant overstorey trees (using two measures including the relative frequency and the diameter at breast height) and two defoliation levels of box trees (including dead and healthy box trees). Our classification workflow applied a GA to simultaneously derive optimal vegetation indices (VIs) and tuning parameters of the SVM. Then the distribution of box tree defoliation was mapped by an SVM ensemble with bagging using GA-optimized VIs and radar data. The GA results revealed that normalized difference vegetation index, red edge normalized difference vegetation index and green normalized difference vegetation index were appropriate for box tree defoliation mapping. An additional comparison of GA-SVM (using GA-optimized VIs and tuning parameters) with a simple SVM (using all VIs and user-based tuning parameters) showed that our suggested workflow performs notably better than the simple SVM (overall accuracy of 0.79 vs 0.74). Incorporating Sentinel-1 data to GA-SVM, marginally improved the performance of the model (overall accuracy: 0.80). The SVM ensemble model using Sentinel-2 and -1 data yielded high accuracies and low uncertainties in mapping of box tree defoliation. The results showed that infested box trees were mostly located at low elevations, low slope and facing north. We conclude that mortality of evergreen understorey tree species can be mapped with good accuracies using freely available satellite data if a suitable work-flow is applied.