receiver operating characteristic

Identification and mapping of tree species at single-tree levels using remotely sensed data is important in sustainable forest management. On the other hand, UAVs provide possibilities to acquire remotely sensed data with high spatial and temporal resolution that facilitate monitoring single tree and assessing quantitative and qualitative characteristics of trees such as species types. Methods based on machine learning can identify species types on UAV colour images but not with high accuracies. Additionally, similarity of trees in visible wavelengths registered on UAV colour images causes errors in species recognition. Therefore, this study was aimed to evaluate combination of UAV-based colour imagery and digital surface model (DSM) and deep learning algorithms in species recognition of a Pistacia-Amygdalus stand.

A part of Pistacia-Amygdalus stands in the wild pistachio research forest with an area of 24 ha was selected for this study. The study area was covered by 649 colour images with spatial resolution of 3.5 cm in 16 flight lines acquired by a Phantom 4Pro UAV. In addition to orthophoto, the DSM with similar spatial resolution was obtained by inverse distance weighted (IDW) method. The colour images and the combination of colour images and DSM were then used by Convolutional Neural Networks (CNNs) to identify Pistacia trees and Amygdalus shrubs. Results were evaluated by indices of accuracy assessment (e.g., accuracy, area under curve (AUC) of receiver operating characteristics).

The results on colour images showed that Amygdalus shrubs (accuracy=0.77, AUC=0.82) were identified with slightly higher accuracy than Pistacia trees (accuracy=0.76, AUC=0.80). Moreover, the results on combination of colour images and DSM showed that Pistacia trees (accuracy=0.85, AUC=0.85) were identified with higher accuracy than Amygdalus shrubs (accuracy=0.81, AUC=0.83). The final map was consisted of 455 Pistacia trees and 1951 Amygdalus shrubs. In addition, the visual interpretation of results revealed that species recognition on the combination of colour images and DSM had higher accuracy despite almost similar values of the indices of accuracy assessment.

In general, the present study explored that combination of colour images and DSM can facilitate species recognition of Pistacia trees and Amygdalus shrubs. Furthermore, this study confirms the capability of CNN in mapping of species at the individual level.