Drought on the fly: UAS mapping of conifer drought stress

Abstract

Foliar moisture content (FMC) plays a crucial role in arid, frequent-fire forests by influencing fire behavior, tree survival, and serving as a proxy for tree health. Management actions such as mechanical thinning, flammable fuels reduction, and prescribed fires can act to mitigate wildfire risk and improve forest health by reducing fuel connectivity, diversifying forest structure, and creating canopy gaps. These management actions could be improved by the targeted removal of moisture stressed and potentially more flammable trees during treatments. However, maps of FMC are not widely available at the tree-level, with no existing products to map FMC of western conifers at the stand-level (< 0.25 m). This project focuses on the scalability of laboratory-developed models predicting sapling FMC to develop and assess tree-level FMC in natural forest conditions. Specifically, we tested whether existing FMC models accurately predict individual tree FMC using uncrewed aerial system (UAS) data. While laboratory-developed models did not translate well to field sites, a field-developed model achieved a mean absolute error of 6.5% FMC and 81% of predictions were within 10% FMC of the observed value. In ranking the observed and predicted FMC values, the field model successfully classified trees into FMC categories with up to 85.9% accuracy. This successful classification demonstrates the utility of UAS-based FMC mapping to inform management prescriptions. Integrating UAS-derived FMC monitoring into forest management could enhance forest resilience and adaptive capacity.