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.

Fertilization is a common practice to increase the productivity and the stand value in the southeastern US. The decision to fertilize a given site is driven by site characteristics and the expected magnitude of response. To determine the magnitude, forest researchers typically rely on fertilization trials established throughout the region of interest and derive growth equations to reflect an increase in either site index or volume. Such equations lack an explicit spatial prediction component. To bridge this gap, we developed a modeling framework that explicitly evaluates the likelihood of a fertilization response as a binary process and the magnitude of such response as a separate model. The methodology relies on the non-parametric interpolator thin plate spines. To test the efficacy of this framework, both percent volume and dominant height response to repeated fertilizer treatments were estimated using data from long-term research trials in Georgia. Several environmental covariates were evaluated on their ability to reduce the models’ root mean square error and account for more of the variation in percent gain from fertilization regimes. Results showed that the inclusion of such covariates improved the model performance and reduced errors associated with interpolation. Thresholding expected responses from fertilization treatments allows practitioners to evaluate the probability of achieving a given response.