A Spatiotemporal Change Detection Method for Monitoring Pine Wilt Disease in a Complex Landscape Using High-Resolution Remote Sensing Imagery

Biyao Zhang,Wei Lu,Wenjiang Huang,Zhuoqing Hao,Bo Wu,Hong Sun,Huichun Ye

doi:10.3390/rs13112083

Abstract

Using high-resolution remote sensing data to identify infected trees is an important method for controlling pine wilt disease (PWD). Currently, single-date image classification methods are widely used for PWD detection in pure stands of pine. However, they often yield false detections caused by deciduous trees, brown herbaceous, and sparsely vegetated regions in complex landscapes, resulting in low user accuracies. Due to the limitations on the bands of the high-resolution imagery, it is difficult to distinguish wilted pine trees from such easily confused objects when only using the optical spectral characteristics. This paper proposes a spatiotemporal change detection method to reduce false detections in tree-scale PWD monitoring under a complex landscape. The framework consisted of three parts, which represent the capture of spectral, temporal, and spatial features: (1) the Normalized Green–Red Difference Index (NGRDI) was calculated as a descriptor of canopy greenness; (2) two NGRDI images with similar dates in adjacent years were contrasted to obtain a bitemporal change index that represents the temporal behaviors of typical cover types; and (3) a spatial enhancement was performed on the change index using a convolution kernel matching the spatial patterns of PWD. Finally, a set of criteria based on the above features were established to extract the wilted pine trees. The results showed that the proposed method effectively distinguishes wilted pine trees from other easily confused objects. Compared with single-date image classification, the proposed method significantly improved user’s accuracy (81.2% vs. 67.7%) while maintaining the same level of producer’s accuracy (84.7% vs. 82.6%).

Highlights

We established a spatiotemporal change detection framework to capture the spatial and temporal patterns of the wilting process caused by pine wilt disease (PWD) (Figure 3): (1) a spectral index was calculated for the two remote sensing images with similar dates in adjacent years; (2) a bi-temporal change analysis was used to obtain the differences between the calculated indices; and (3) the resulting image was enhanced through spatial convolution based on a proposed kernel that was fitted to the spatial pattern of the wilted pine trees
The Normalized Green–Red Difference Index (NGRDI) observations in 2018 and 2019 for six typical cover types, including wilted pine tree caused by PWD in 2019 as well as healthy pine tree, deciduous tree, grass, crop, and barren, were used to construct a scatter plot (Figure 6)
The NGRDI observations for 2018 and 2019 were very close if there was no land cover change, while those for pine trees infected with PWD in 2019 clearly deviated from the 1:1 line, mostly falling in the fourth quadrant of the coordinate system

Summary

Introduction

Pine wilt disease (PWD) is a lethal wilting disease caused by the pine wood nematode (Bursaphelenchus xylophilus; PWN). After becoming infected with the disease, pine trees show certain symptoms, in which the needles of the tree gradually change color; the pine resin stops flowing; and the tree wilts, withers, and eventually dies, with the whole process occurring over a few months [1,2,3]. In 2020, PWD has been reported in 18 provinces, autonomous regions, and municipalities that are directly under the Central Government of. With an infestation area of 1.8 million ha and a death toll of 19.5 million trees [4].

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