Abstract
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.
Highlights
Foliage biomass is an ecological parameter often used as an indicator of forest productivity in commercial forest plantations [1,2]
Normalized Difference Moisture Index (NDMI) was a better predictor of leaf area index (LAI) in loblolly pine plantations in the southeastern United States when compared to more commonly used vegetation indices, such as normalized difference vegetation index (NDVI)
This study used error-in-variable methods to reduce the effects of measurement errors in a leaf area index estimation model from Landsat 5 and 7 satellite imagery
Summary
Foliage biomass is an ecological parameter often used as an indicator of forest productivity in commercial forest plantations [1,2]. Over the last two decades, use of remotely sensed data has increased as products become more readily available, allowing commercial forest plantation managers to use it as part of their decision-making process [6,7,8,9]. ( loblolly pine) plantations are some of the applications for these data [10,11,12,13,14,15,16,17]. In application, most indirect methods aim at estimating leaf area index (LAI). LAI, the onesided leaf surface area over a fixed ground area, is a biophysical parameter that has been used to understand productivity drivers in loblolly pine stands [1,18,19,20].
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