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

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%).

Automated summary

This study proposes a spatiotemporal change detection method to reduce false detections in tree-scale pine wilt disease monitoring in complex landscapes. The method captures spectral, temporal, and spatial features through calculating the Normalized Green-Red Difference Index, contrasting NGRDI images of similar dates in adjacent years, and enhancing the change index using a convolution kernel. The proposed method effectively distinguishes wilted pine trees from other easily confused objects, improving user's accuracy while maintaining the same level of producer's accuracy compared to single-date image classification.