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.

摘要：

背景：本研究旨在评估巴西南部火炬松（Pinus taeda L.）林分的无人机（Unmanned Aerial Vehicle, UAV）影像，以分类被黑帽悬猴（Sapajus nigritus）侵害的林木。研究于2018年3月开展，使用DJI Phantom Pro 4无人机在18.73公顷的研究区域内获取影像。本研究评估了多种波段组合与植被指数，基于树冠颜色的目视解译结合野外实测数据，将林木手动标注为未受侵害、枯死及受侵害三类。将分类后的林木划分为训练集（占比75%）与验证集（占比25%），同时设置三种树冠直径尺度（0.5、1及1.5米）与三种面向区域的分类算法。分类精度通过总体精度与Kappa系数进行评估。

结果：共计手动检测到3773株林木，其中39%受侵害、5%枯死、56%未受侵害。结果同时表明，最优树冠直径尺度为0.5米，最优分类算法为支持向量机（SVM），最高分类精度由超绿指数（Excess Green Index, ExG）与原始光谱波段的组合实现。

结论：本研究证实，可借助无人机影像开展林木侵害监测，该研究结果可为森林经营管理相关举措提供参考依据。