Appraisal of arp images and machine learning to detect Sapajus nigritus attacks on loblolly’s pine stands in Southern Brazil

ABSTRACT

Background:

This study aimed to evaluate UAV images of Pinus taeda L. stands for classifying trees attacked by Sapajus nigritus in Southern Brazil. UAV images were acquired on March 2018, using a DJI Phantom Pro 4 over 18.73 hectares. We evaluated different band compositions and vegetation indices. Using photo interpretation based on the color of the crown and field measurements, the trees were manually labeled as not attacked, dead, and attacked. The classified trees were divided into training (75%) and validation (25%), considering three tree crown diameters (0.5, 1, and 1.5 m) and three region-oriented classification algorithms. The classification accuracy was assessed by overall accuracy and the kappa index.

Results:

A total of 3,773 trees were manually detected, of which 39% were attacked, 5% died and 56% were not attacked. The results also indicated that the best-chosen diameter was 0.5 meters, the best classifier algorithm was the SVM, and the highest accuracy was represented by the composition of the ExG index associated with the original spectral bands.

Conclusion:

We argue that the attacks can be monitored using UAV images and such results provide insights for forest management initiatives.

Key words:
UAV; photogrammetry; forest health.

HIGHLIGHTS

The ExG was the best index for analyzing the forest health of Pinus taeda L.