Prediction of Freeze Damage and Minimum Winter Temperature of the Seed Source of Loblolly Pine Seedlings Using Hyperspectral Imaging. Issue 3 (2nd April 2021)

• Record Type: Journal Article
• Authors: Lu, Yuzhen; Walker, Trevor D; Acosta, Juan J; Young, Sierra; Pandey, Piyush; Heine, Austin J; Payn, Kitt G
• Is Part Of: Forest science. Volume 67:Issue 3(2021)
• Page Start: 321
• Page End: 334
• Publication Date: 2021-04-02
• Subjects: loblolly pine -- freeze damage -- hyperspectral imaging -- predictive modeling -- variable selection; Forests and forestry -- Periodicals; Forest management -- Periodicals; Forest policy -- United States -- Periodicals; Forest management; Forest policy; Forests and forestry; United States; Electronic journals; Periodicals; 634.9
• DOI: 10.1093/forsci/fxab003
• Languages: English
• ISSNs: 0015-749X

Abstract

Abstract: The most important climatic variable influencing growth and survival of loblolly pine is the yearly average minimum winter temperature (MWT) at the seed source origin, and it is used to guide the transfer of improved seed lots throughout the species' distribution. This study presents a novel approach for the assessment of freeze-induced damage and prediction of MWT at seed source origin of loblolly pine seedlings using hyperspectral imaging. A population comprising 98 seed lots representing a wide range of MWT at seed source origin was subjected to an artificial freeze event. The visual assessment of freeze damage and MWT were evaluated at the family level and modeled with hyperspectral image data combined with chemometric techniques. Hyperspectral scanning of the seedlings was conducted prior to the freeze event and on four occasions periodically after the freeze. A significant relationship ( R 2 = 0.33; p < .001) between freeze damage and MWT was observed. Prediction accuracies of freeze damage and MWT based on hyperspectral data varied among seedling portions (full-length, top, middle, and bottom portion of aboveground material) and scanning dates. Models based on the top portion were the most predictive of both freeze damage and MWT. The highest prediction accuracy of MWT [RPD (ratio of prediction to deviation) = 2.12, R 2 = 0.78] was achieved using hyperspectral data obtained prior to the freeze event. Adoption of this assessment method would greatly facilitate the characterization and deployment of well-adapted loblolly pine families across the landscape.