Abstract

Forest management at a sub-stand level will be a critical part of optimizing future productivity gains for timber or carbon objectives. We developed a methodology to apply “precision forestry” on one-hectare grids in operational loblolly pine (Pinus taeda L.) plantations in east Texas to quantify responses to site-specific nutrient management and vegetation control based on measures of leaf area index (LAI). First, we cross-validated satellite-derived (Sentinel-2) LAI with airborne laser scanning (ALS)-derived LAI and found the strongest linear relationship with the Above and Below Ratio Index (ABRI) method (R2 = 0.83). Five post-thin midrotation stands, totaling 256 ha, were divided one year prior to fertilization with one part receiving herbicide treatment for competition control. Fertilization was applied in a grid pattern via fixed-wing aircraft with rates (0 to or 336 kg N ha?1 + 33.6 kg P ha?1) determined based either on LAI derived from satellite imagery or randomly assigned. We fit a linear mixed effect model (conditional R2 = 0.83) to predict the change in seasonal peak LAI from pre-fertilization (October 2020) to one growing season post-fertilization (October 2021). Main effects of LAI-based vs. random application, average understory height, N rate applied, and herbicide application status were all significant (p < 0.05). Increases in LAI were greater where fertilizer rates were based on remotely sensed LAI prior to application as compared to randomly assigned rates. Increases in LAI also correlated to increases in fertilizer application rate. Areas with understory vegetation below 3 m in height exhibited greater increases in LAI following fertilization. The effects of fertilizer can persist for multiple years, thus the long-term impacts of variable-rate fertilization in operational pine plantations, and in combination with vegetation control, will require additional time to assess the return on investment for these management practices. This study demonstrates the feasibility and potential efficiency gains available by informing treatment planning at a sub-stand scale with remotely sensed canopy metrics. © 2025 Elsevier B.V.