Sentinel-2 Leaf Area Index Estimation for Pine Plantations in the Southeastern United States

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.

A Model to Estimate Leaf Area Index in Loblolly Pine Plantations Using Landsat 5 and 7 Images

“…While the weighted average of the pixels falling within the study plot were used to calculate the VIs, a further potential source of errors may come from interference in reflectance from areas surrounding the study plot under evaluation. Results from [90] corroborate the general underprediction of [11], thus the model presented in this research (Equation (4)) should provide an update to sites relying on legacy data from Landsat 5 TM and 7 ETM+. Further improvements in leaf area index predictions may be realized through the use of advanced machine learning techniques, but the ease of use was the driving force behind the development of the model proposed in this research.…”

Using GEDI as training data for an ongoing mapping of landscape-scale dynamics of the plant area index

“…Other study design components, that complicate a direct comparison include, study areas, vegetation types, study seasons, variance in the data, validation strategies and other aspects of the study design that vary considerably between publications. In addition, RMSE/sd values which facilitate a comparison across studies are rarely communicated and many previous studies restricted data analysis to plots that were clearly dominated by a single species (Brown et al 2019), or featured homogeneous cover (Korhonen et al 2017, Cohrs et al 2020. Nevertheless, it is helpful to put the results of this study in the context of previous research on LAI estimation.…”

Modeling leaf area index using time-series remote sensing and topographic data in pure Anatolian black pine stands

“…On the other hand, our study demonstrated that NDVI variable was not included as an independent variable in the models. Cohrs et al ( 2020 ) found that simple ratio indices were the most important variable for LAI prediction. Their study indicated good performance ( R 2 = 0.81) at predicting the LAI for loblolly pine stands.…”

“…Many statistical models are used to estimate LAI based on remote sensing data, and they can be classified into two groups: (1) parametric linear models (Günlü et al 2017 ; Meyer et al 2019 ) and nonlinear models (Zhang and Song 2021 ), and (2) nonparametric models such as artificial neural networks (Xie et al 2021 ), random forest (Vafaei et al 2021 ), and support vector machines (Verrelst et al 2012 ; Cohrs et al 2020 ). Multiple linear regression (MLR) is the most commonly used method in LAI estimation studies (Soudani et al 2006 ; Günlü et al 2017 ; Guo et al 2021 ; Wang et al 2022 ).…”