Characterizing annual leaf area index changes and volume growth using ALS and satellite data in forest plantations

Abstract

While Leaf Area Index (LAI) is critical for understanding forest canopy, photosynthesis and forest growth, traditional field-based LAI measurements are laborious and costly. Remote sensing offers a practical alternative for extensive assessments. Satellite imagery provides broad-scale, long-term monitoring; however, may lack detail needed to guide specific forest management actions. Conversely, Airborne Laser Scanning (ALS) provides accurate LAI estimates at fine spatial detail but is limited by cost and temporal monitoring constraints. Combining ALS data with satellite observations could enhance plantation management decisions by balancing extensive coverage with detailed observations. This study explores the integration of ALS and satellite remote sensing as a comprehensive alternative for assessing LAI and stand volume growth rate (m³/ha/year) in operational Pinus radiata plantations in central-south Chile. Our approach comprised four major steps. First, we applied the Beer-Lambert law using ALS vertical profiles to estimate LAI across a forest plantation (LAI_ALS). We found that ALS accurately estimated LAI across 121 plots (R² = 0.82 and RMSE = 0.51). Second, we built a simple linear regression to link LAI_ALS with the Normalized Difference Moisture Index (NDMI) derived from surface reflectance information from the Landsat/Sentinel-2 satellites, resulting in an R² of 0.53 and an RMSE of 1.17. This step showed a higher correlation with satellite data compared to using only ground-based LAI estimates (R² = 0.38; RMSE = 1.18). Third, we transformed biweekly NDMI time series to LAI, then derived peak annual LAI as an indicator of mean annual increment (MAI) (R² = 0.51; RMSE = 5.27 m³/ha/year). This allowed us to characterize stand growth and LAI on a yearly wall-to-wall basis. Throughout the modelling steps, we incorporated error propagation, allowing final estimates to be error bounded. This integrated approach serves as a tool for identifying and visualizing growth irregularities, guiding adaptive management strategies to maintain or enhance stand productivity over time.