Landsat-based monitoring of southern pine beetle infestation severity and severity change in a temperate mixed forest

The recent northward expansion of Southern Pine Beetle (SPB) outbreaks associated with warming winters has caused extensive tree mortality in temperate pine forests, significantly affecting forest dynamics, structure, and functioning. Spatially-explicit early warning and detection of SPB-induced tree mortality is critical for timely and sustainable forest management practices. The unique contributions of remote sensing technologies to mapping the location, extent, and severity of beetle outbreaks, as well as assisting in analyzing the potential drivers for outbreak predictions, have been well recognized. However, little is known about the performance of moderate resolution satellite multispectral imagery for early warning and detection of SPB-induced tree mortality. Thus, we conducted this study, as the first attempt, to capture the spatial-temporal patterns of SPB infestation severity at the regional scale and to understand the underlying environmental drivers in a spatially-explicit manner. First, we explored the spectral signatures of SPB-killed trees based on 30-m plot measurements and Landsat-8 imagery. Then, to improve detection accuracy for areas with low-moderate SPB infestation severity, we added spectral-temporal anomaly information in the form of a linear trend of the spectral index trajectory to a previously developed approach. The best overall accuracy increased from 84.7% to 90.1% and the best Macro F1 value increased from 0.832 to 0.900. Next, we compared the performances of spectral indices in mapping SPB infestation severity (i.e., % red stage within the 30-m grid cell). The results showed that the combination of Normalized Difference Moisture Index and Tasseled Cap Greenness had the best performance for mapping SPB infestation severity (2016: R2 = 0.754; RSME = 15.7; 2017: R2 = 0.787; RSME = 12.4). Finally, we found that climatic and landscape variables can explain the detected patterns of SPB infestation from 2014 to 2017 in our study area (R2 = 0.751; RSME = 9.67), providing valuable insights on possible predictors for early warning of SPB infestation. Specifically, in our study area, winter dew point temperature was found to be one of the most important predictors, followed by SPB infestation locations in the previous year, canopy cover of host species, elevation, and slope. In the context of continued global warming, our study not only provides a novel framework for efficient, spatially-explicit, and quantitative measurements of forest damage induced by SPB infestation over large scales, but also uncovers opportunities to predict future SPB outbreaks and take precautions against it.