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.

We examined three hypotheses in a nutrient dose and application frequency study installed in juvenile (aged 2–6 years old) Pinus stands at 22 sites in the southeastern United States. At each site, eight or nine treatments were installed where nitrogen was applied at different rates (0, 67, 134, 268 kg ha⁻¹) and frequencies (0, 1, 2, 4 and 6 years) in two or four replications. Phosphorus was applied at 0.1 times the nitrogen rate and other elements were added as needed based on foliar nutrient analysis to insure that nutrient imbalances were not induced with treatment. Eight years after treatment initiation, the site responses were grouped based on texture and drainage characteristics: soil group 1 consisted of poorly drained soils with a clayey subsoil, group 2 consisted of poorly to excessively drained spodic soils or soils without a clay subsoil, and group 3 consisted of well-drained soils with a clayey subsoil. We accepted the first hypothesis that site would be a significant factor explaining growth responses. Soil group was also a significant factor explaining growth response. We accepted our second hypothesis that the volume growth-cumulative dose response function was not linear. Volume growth reached an asymptote in soil groups 1 and 3 between cumulative nitrogen doses of 300–400 kg ha⁻¹. Volume growth responses continued to increase up to 800 kg ha⁻¹ of cumulatively applied nitrogen for soil group 2. We accepted our third hypothesis that application rate and frequency did not influence the growth response when the cumulative nitrogen dose was equivalent. There was no difference in the growth response for comparisons where a cumulative nitrogen dose of 568 kg ha⁻¹ was applied as 134 kg ha⁻¹ every two years or as 269 kg ha⁻¹ every four years, or where 269 kg ha⁻¹ of nitrogen was applied as four applications of 67 kg ha⁻¹ every two years or as two applications of 134 kg ha⁻¹ every four years. Clearly, the sites examined here were limited by nitrogen and phosphorus, and applications of these elements to young stands effectively ameliorated these limitations. However, there were differences in the response magnitude that were related to soil texture and drainage. Juvenile fertilizer applications resulted in high stocking levels early in the rotation; this condition should be considered when undertaking juvenile fertilization programs.

This study presents forest fertilization and carbon sequestered from fertilization in the southeastern United States in the context of newly available silviculture management information. Maximum annual fertilized forest area in the southeastern United States occurred in 1999 when 1.58 million acres were fertilized. Since then, the fertilized area has generally declined, and in 2016, 589,000 acres were fertilized. This decline is likely related to new research and changes in economic conditions. Recent research has determined that lower, more frequent nutrient doses gave the same biological response as larger, less frequent doses; improved our understanding and use of urease inhibitors; and quantified the upper limit to pine productivity in the southeast United States. All of these factors combined with economic concerns including the continued low number of housing starts, a large inventory of stands with trees that are already sawtimber size, and low sawtimber prices influence forest managers’ decisions about fertilization. However, fertilization increases carbon sequestration in forests, and carbon markets that recognize this contribution to sequestration are developing and may provide additional income to forest managers and, ultimately, increase the area fertilized.

We examined fertilization (224 and 56 kg ha⁻¹ of elemental nitrogen and phosphorus, applied as urea and diammonium phosphate, respectively) and vegetation control (one-time site-specific application) in a 2 × 2 factorial design with three or four replicates at each site on 13 sites (10 in Pinus taeda and 3 in Pinus elliottii). Nitrogen and phosphorus limited pine growth on seven sites where we found significant volume growth responses to fertilization in at least one measurement period. Five sites had significant volume growth responses to vegetation control in at least one measurement period. Biologic response treatment order was fertilizer plus vegetation control > fertilizer > vegetation control. The combined treatment effects were additive, indicating that resources other than nitrogen and phosphorus were being ameliorated by the vegetation control. Vegetation control response was not related to estimates of competing vegetation basal area, and its duration was likely limited by regrowth of competing vegetation on some sites. Competing vegetation leaf area was proposed as a good metric by which to estimate the extent of interference of noncrop vegetation. We recommend that future work examining vegetation control focus on a process approach to better understand the influences of competing vegetation on crop tree growth.

Vegetation indices calculated from remotely sensed satellite imagery are commonly used within empirically derived models to estimate leaf area index in loblolly pine plantations in the southeastern United States. The data used to parameterize the models typically come with observation errors, resulting in biased parameters. The objective of this study was to quantify and reduce the effects of observation errors on a leaf area index (LAI) estimation model using imagery from Landsat 5 TM and 7 ETM+ and over 1500 multitemporal measurements from a Li-Cor 2000 Plant Canopy Analyzer. Study data comes from a 16 quarter 1 ha plot with 1667 trees per hectare (2 m × 3 m spacing) fertilization and irrigation research site with re-measurements taken between 1992 and 2004. Using error-in-variable methods, we evaluated multiple vegetation indices, calculated errors associated with their observations, and corrected for them in the modeling process. We found that the normalized difference moisture index provided the best correlation with below canopy LAI measurements (76.4%). A nonlinear model that accounts for the nutritional status of the stand was found to provide the best estimates of LAI, with a root mean square error of 0.418. The analysis in this research provides a more extensive evaluation of common vegetation indices used to estimate LAI in loblolly pine plantations and a modeling framework that extends beyond the typical linear model. The proposed model provides a simple to use form allowing forest practitioners to evaluate LAI development and its uncertainty in historic pine plantations in a spatial and temporal context.