Individual Tree-Crown Detection and Species Identification in Heterogeneous Forests Using Aerial RGB Imagery and Deep Learning

Automatic identification and mapping of tree species is an essential task in forestry conservation. However, applications that can geolocate individual trees identify their heterogeneous forests on a large scale are lacking. Here, we assessed the potential Convolutional Neural Network algorithm, Faster R-CNN, which efficient end-to-end object detection approach, combined with open-source aerial RGB imagery for geolocation upper canopy layer temperate forests. We studied four species, i.e., Norway spruce (Picea abies (L.) H. Karst.), silver fir (Abies alba Mill.), Scots pine (Pinus sylvestris L.), European beech (Fagus sylvatica growing To fully explore approach identification, trained single-species multi-species models. For models, average accuracy (F1 score) was 0.76. Picea detected highest accuracy, F1 0.86, followed by A. = 0.84), F. 0.75), Pinus 0.59). Detection increased models 0.92), while it remained same or decreased slightly other species. Model performance more influenced site conditions, such as forest stand structure, less illumination. Moreover, misidentification number included increased. In conclusion, presented method accurately map location may serve basis future inventories targeted management actions to support resilient