Potential of Unmanned Aerial Vehicle Red–Green–Blue Images for Detecting Needle Pests: A Case Study with Erannis jacobsoni Djak (Lepidoptera, Geometridae)

Abstract

Erannis jacobsoni Djak (Lepidoptera, Geometridae) is a leaf-feeding pest unique to Mongolia. Outbreaks of this pest can cause larch needles to shed slowly from the top until they die, leading to a serious imbalance in the forest ecosystem. In this work, to address the need for the low-cost, fast, and effective identification of this pest, we used field survey indicators and UAV images of larch forests in Binder, Khentii, Mongolia, a typical site of Erannis jacobsoni Djak pest outbreaks, as the base data, calculated relevant multispectral and red–green–blue (RGB) features, used a successive projections algorithm (SPA) to extract features that are sensitive to the level of pest damage, and constructed a recognition model of Erannis jacobsoni Djak pest damage by combining patterns in the RGB vegetation indices and texture features (RGBVI&TF) with the help of random forest (RF) and convolutional neural network (CNN) algorithms. The results were compared and evaluated with multispectral vegetation indices (MSVI) to explore the potential of UAV RGB images in identifying needle pests. The results show that the sensitive features extracted based on SPA can adequately capture the changes in the forest appearance parameters such as the leaf loss rate and the colour of the larch canopy under pest damage conditions and can be used as effective input variables for the model. The RGBVI&TF-RF440 and RGBVI&TF-CNN740 models have the best performance, with their overall accuracy reaching more than 85%, which is a significant improvement compared with that of the RGBVI model, and their accuracy is similar to that of the MSVI model. This low-cost and high-efficiency method can excel in the identification of Erannis jacobsoni Djak-infested regions in small areas and can provide an important experimental theoretical basis for subsequent large-scale forest pest monitoring with a high spatiotemporal resolution.

Identification of Damaged Canopies in Farmland Artificial Shelterbelts Based on Fusion of Unmanned Aerial Vehicle LiDAR and Multispectral Features

“…They calculated the ratio of white pixels to total pixels within the canopy using a simple GIS model as a basis for assessing the damage level of individual trees, achieving a maximum average accuracy of 94% in detecting damage to individual fir trees in the Zao Mountains of Japan caused by bark beetle attacks. Bai et al [19] extracted features sensitive to pest damage severity using a continuous projection algorithm (SPA) from UAV visible-light images, combined with patterns and texture features in RGB vegetation indices, and employed machine learning methods to construct a model for identifying damage caused by Erannis jacobsoni Djak to Larix sibirica, achieving an overall accuracy of over 85%.…”