Abstract
Leaf area index (LAI) is an important biophysical indicator of forest health that is linearly related to productivity, serving as a key criterion for potential nutrient management. A single equation was produced to model surface reflectance values captured from the Sentinel-2 Multispectral Instrument (MSI) with a robust dataset of field observations of loblolly pine (Pinus taeda L.) LAI collected with a LAI-2200C plant canopy analyzer. Support vector machine (SVM)-supervised classification was used to improve the model fit by removing plots saturated with aberrant radiometric signatures that would not be captured in the association between Sentinel-2 and LAI-2200C. The resulting equation, LAI = 0.310SR − 0.098 (where SR = the simple ratio between near-infrared (NIR) and red bands), displayed good performance ( R 2 = 0.81, RMSE = 0.36) at estimating the LAI for loblolly pine within the analyzed region at a 10 m spatial resolution. Our model incorporated a high number of validation plots (n = 292) spanning from southern Virginia to northern Florida across a range of soil textures (sandy to clayey), drainage classes (well drained to very poorly drained), and site characteristics common to pine forest plantations in the southeastern United States. The training dataset included plot-level treatment metrics—silviculture intensity, genetics, and density—on which sensitivity analysis was performed to inform model fit behavior. Plot density, particularly when there were ≤618 trees per hectare, was shown to impact model performance, causing LAI estimates to be overpredicted (to a maximum of X i + 0.16). Silviculture intensity (competition control and fertilization rates) and genetics did not markedly impact the relationship between SR and LAI. Results indicate that Sentinel-2’s improved spatial resolution and temporal revisit interval provide new opportunities for managers to detect within-stand variance and improve accuracy for LAI estimation over current industry standard models.
Highlights
Pine is the most commercially important tree species in the southeastern United States [1]
Growth efficiency as well as leaf area index (LAI) have been considered across sites with variability such as these before, with responses to silviculture being most apparent on nutrient-deficient, poorly drained sites like RW20-NC [40]
We reported a statistically significant interaction between stand density (≤618 trees per hectare) and model performance, though the implications of a density interaction was deemed operationally insignificant to warrant adjusting the model
Summary
Pine is the most commercially important tree species in the southeastern United States [1]. Within this region, inherent soil nutrient deficiencies of nitrogen (N) and phosphorous (P) are very common [2]. The addition of fertilizer is often a necessary silvicultural practice both at the time of planting and at mid-rotation. Fertilization— with N—has a positive correlation with leaf area index (LAI), a dimensionless ratio quantifying projected leaf surface area per unit ground area. For intensively managed loblolly pine, LAI serves as an indicator of nutrient status and potential future volume growth [3]. Higher LAIs correspond with a greater capacity to intercept light, photosynthesize and fix carbon [4]
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