Airborne Hyperspectral Imaging in Early Monitoring of Pine Wood Nematode
author: Gavin
2022-01-14
Airborne Hyperspectral Imaging in Early Monitoring of Pine Wood Nematode
Pine wood nematode (PWD, the "cancer" of pine trees) is one of the devastating international forest diseases caused by the pine wood nematode (PWN; Bursaphelenchus xylophilus), which has caused huge ecological and economic losses to our country. An effective way to prevent large outbreaks of PWD is to detect and remove damaged pine trees in the early stages of PWD infection. However, in the visible wavelength range, early-infected pines fail to show obvious morphological or color changes, making early detection of PWD difficult.
Principle:
UAV-based hyperspectral imaging has great potential for early detection of PWD. Commonly used methods such as three-dimensional convolutional neural networks (3D-CNN) are able to simultaneously extract and fully exploit spatial and spectral information from raw spectral data.
Solution:
ATHL9010 series is the 3rd generation hyperspectral imager integrated LiDAR system.Use ATHL9010 to conduct field experiments to obtain hyperspectral data and radar data.
Reflectance curves for hardwood, early infected pine, and late infected pine.
The recognition and classification results of PWD by different models.
Table 4 Classification accuracy of 3 tree categories by different methods.
The effect of different training sample sizes on the accuracy of 3D-Res CNN models.
Pine wood nematode (PWD, the "cancer" of pine trees) is one of the devastating international forest diseases caused by the pine wood nematode (PWN; Bursaphelenchus xylophilus), which has caused huge ecological and economic losses to our country. An effective way to prevent large outbreaks of PWD is to detect and remove damaged pine trees in the early stages of PWD infection. However, in the visible wavelength range, early-infected pines fail to show obvious morphological or color changes, making early detection of PWD difficult.
Principle:
UAV-based hyperspectral imaging has great potential for early detection of PWD. Commonly used methods such as three-dimensional convolutional neural networks (3D-CNN) are able to simultaneously extract and fully exploit spatial and spectral information from raw spectral data.
Solution:
ATHL9010 series is the 3rd generation hyperspectral imager integrated LiDAR system.Use ATHL9010 to conduct field experiments to obtain hyperspectral data and radar data.
Result:
Reflectance curves for hardwood, early infected pine, and late infected pine.
The recognition and classification results of PWD by different models.
Table 4 Classification accuracy of 3 tree categories by different methods.
The effect of different training sample sizes on the accuracy of 3D-Res CNN models.
Conclusion:
In this study, the authors applied residual blocks to 3D CNN based on hyperspectral images and constructed a 3D-Res CNN model for early detection of PWD. Furthermore, the classification accuracy of 2D-CNN, 3D-CNN, 2D-Res CNN, and 3D-Res CNN models for identifying PWD-infected pine trees was compared. The results show that the 3D-Res CNN model is the most effective. It can extract spectral information and spatial information from hyperspectral images at the same time. Although the training time is long, it can still meet the needs of large-scale practical forestry applications.
Related Products:
Drone Hyperspectral Imaging System:ATHL9010
Airborne Hyperspectral Remote Sensing System:ATH9010
In this study, the authors applied residual blocks to 3D CNN based on hyperspectral images and constructed a 3D-Res CNN model for early detection of PWD. Furthermore, the classification accuracy of 2D-CNN, 3D-CNN, 2D-Res CNN, and 3D-Res CNN models for identifying PWD-infected pine trees was compared. The results show that the 3D-Res CNN model is the most effective. It can extract spectral information and spatial information from hyperspectral images at the same time. Although the training time is long, it can still meet the needs of large-scale practical forestry applications.
Related Products:
Drone Hyperspectral Imaging System:ATHL9010
Airborne Hyperspectral Remote Sensing System:ATH9010
Using ATHL9010 to estimate above-ground carbon stocks at the single-tree scale in subtropical forests
A Study on Eutrophication of Lake Based on Hyper-spectraI Remote
Related Article
Discover how UAV‑based hyperspectral imaging and deep learning can monitor SDI, TOC, and TEP – key membrane fouling indicators for seawater desalination during harmful algal blooms. Based on a recent Water Research study, this article explores how Optosky's ATH9010 enables spatial risk mapping and proactive intake management.
Applications | ATH9010 Enables Water Quality and Membrane Fouling Monitoring for Desalination During Algal Blooms
Choosing between 785 nm and 1064 nm for Raman? This guide explains the physics of fluorescence, compares signal strength and fluorescence suppression, and provides a step‑by‑step decision tree. Real‑world drug detection and pesticide examples show why wavelength matters – and how dual‑wavelength coverage offers the ultimate solution.
785 nm or 1064 nm? Fluorescence Is the Dividing Line
Inspired by the 2026 Science paper from Zhang Jun’s team on a video‑rate on‑chip hyperspectral microsystem, Optosky’s ATH series moves beyond lab‑only tools. We deliver tailored, real‑world hyperspectral systems that integrate compact hardware, edge AI, and application‑specific algorithms—turning raw spectral data into actionable decisions, right where you need them.
From Science to the Field: Custom Hyperspectral Solutions by Optosky
See how 40 Optosky NY3300Pro multispectral units were deployed across a large‑scale demonstration farmland in Northwest China. With 9‑band imaging, solar power, 4G transmission, and automated data analytics, this solution enables precise growth monitoring, early pest detection, and water‑fertiliser optimisation – a true leap toward smart agriculture.
Case Study | Bulk NY3300Pro Installation Transforms Smart Farming