Retrieving the Infected Area of Pine Wilt Disease-Disturbed Pine Forests from Medium-Resolution Satellite Images Using the Stochastic Radiative Transfer Theory

Abstract

Pine wilt disease (PWD) is a global destructive threat to forests which has been widely spread and has caused severe tree mortality all over the world. It is important to establish an effective method for forest managers to detect the infected area in a large region. Remote sensing is a feasible tool to detect PWD, but the traditional empirical methods lack the ability to explain the signals and can hardly be extended to large scales. The studies using physically-based models either ignore the within-canopy heterogeneity or rely too much on prior knowledge. In this study, we propose an approach to retrieve PWD infected areas from medium-resolution satellite images of two phases based on the simulations of an extended stochastic radiative transfer model for forests infected by pests (SRTP). A small amount of prior knowledge was used, and a change of background soil was considered in this approach. The performance was evaluated in different study sites. The inversion method performs best in the three-dimensional model LESS simulation sample plots (R2 = 0.88, RMSE = 0.059), and the inversion accuracy decreases in the real forest sample plots. For Jiangxi masson pine stand with large coverage and serious damage, R2 = 0.57, RMSE = 0.074; and for Shandong black pine stand with sparse and a small number of single plant damage, R2 = 0.48, RMSE = 0.063. This study indicates that the SRTP model is more feasible for pest damage inversion over different regions compared with empirical methods. The stochastic radiative transfer theory provides a potential approach for future monitoring of terrestrial vegetation parameters.

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

“…Different satellite sensors have been utilized to collect forest data across wide geographical areas at various resolutions [32,33]. In medium-resolution satellite imagery for the identification of areas infected by pests and diseases, Li et al [34] proposed an extension of the stochastic radiative transfer model (SRTP) based on Sentinel-2, combining a small amount of prior knowledge with a model simulation and a random forest algorithm to identify areas infected by pests and diseases from medium-resolution remote sensing images. Using high-resolution satellite imagery, Mullen [35] employed WorldView-2 to detect early tree damage caused by mountain pine beetles, achieving an overall accuracy of 75% in distinguishing infested trees from healthy ones.…”

Pine wilt disease detection method based on improved YOLOv5

“…Some studies have used radiation transfer models to detect forest pests and diseases through simulations. For example, combining reflectance and transmittance (free) with reversible forest reflectance model (canopy model) to detect Pinus massoniana (PSB) stress in Yunnan pine forests [3] . However, there are still limitations.…”

Detection Method of Infected Wood on Digital Orthophoto Map–Digital Surface Model Fusion Network

“…Zhang et al [26] propose a method based on multi-temporal remote sensing image comparison to solve the problem of the serious misjudgment of deciduous trees and dead grass. Li et al [27] employ a medium-resolution satellite image analysis and simulations using an extended stochastic radiative transfer model to delineate areas affected by PWD. However, satellite remote sensing methods often achieve low PWD detection accuracy due to the constraints of the low spatial-temporal resolutions, weather complications and the challenge of capturing detailed changes, especially in cases where the number of infected trees in a forest is limited.…”