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<p>The estimation of leaf water content (<i>LWC</i>) is essential for understanding the physiological status of forest species and assessing fire hazards. This study explores the potential of unmanned aerial vehicle UAV-acquired multispectral imagery for <i>LWC</i> estimation in <i>Pinus radiata</i> and <i>Eucal</i><i>yp</i><i>tus globulus</i>. These are two commercially valuable timber species, commonly planted in monospecific stands in mediterranean forests and widely used in similar plantations worldwide, often prone to wildfires. Leaf samples were collected from individual trees located in two plots in the Valparaíso region of Chile. Leaves were brought to the laboratory and <i>LWC</i> estimated through gravimetric methods. Five types of models were trained to estimate LWC from UAV-acquired imagery, including classical and machine learning techniques: Linear Regression, Mixed Linear Regression, Random Forest (<i>RF</i>), Support Vector Regression (<i>SVR</i>), and k-Nearest Neighbors (<i>k-NN</i>). These models were trained to predict <i>LWC</i> based on the spectral reflectance data obtained from the UAV imagery. Results from both <i>LW</i><i>C</i> and spectral models exhibit distinctive physiological traits of each species, with <i>P. radiata</i> showing more conservative water strategies and higher water retention compared to <i>E. globulus</i>. The best-performing model for <i>E. globulus</i> was a mixed linear regression, achieving an R² of 0.69, an RMSE of 9.38, and a bias of -1.44. For <i>P. radiata</i>, the best model was a simple linear regression with an R² of 0.33, an RMSE of 10.29, and a bias of -0.68. The mixed linear regression model of <i>E. globulus</i> also provided more consistent predictions and better spatial mapping of <i>LWC</i> distribution.</p>

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Development of UAV Spectral Models for Estimating Leaf Water Content at Canopy Level for Eucalyptus globulus and Pinus radiata

  • 期刊论文

  • 发表日期：

    2026年2月8日

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  • María Ramírez de Armendaiz,
  • Ignacio Fernández Chicharro,
  • Silvia Merino de Miguel,
  • Pedro Reszka

摘要

The estimation of leaf water content (LWC) is essential for understanding the physiological status of forest species and assessing fire hazards. This study explores the potential of unmanned aerial vehicle UAV-acquired multispectral imagery for LWC estimation in Pinus radiata and Eucalyptus globulus. These are two commercially valuable timber species, commonly planted in monospecific stands in mediterranean forests and widely used in similar plantations worldwide, often prone to wildfires. Leaf samples were collected from individual trees located in two plots in the Valparaíso region of Chile. Leaves were brought to the laboratory and LWC estimated through gravimetric methods. Five types of models were trained to estimate LWC from UAV-acquired imagery, including classical and machine learning techniques: Linear Regression, Mixed Linear Regression, Random Forest (RF), Support Vector Regression (SVR), and k-Nearest Neighbors (k-NN). These models were trained to predict LWC based on the spectral reflectance data obtained from the UAV imagery. Results from both LWC and spectral models exhibit distinctive physiological traits of each species, with P. radiata showing more conservative water strategies and higher water retention compared to E. globulus. The best-performing model for E. globulus was a mixed linear regression, achieving an R² of 0.69, an RMSE of 9.38, and a bias of -1.44. For P. radiata, the best model was a simple linear regression with an R² of 0.33, an RMSE of 10.29, and a bias of -0.68. The mixed linear regression model of E. globulus also provided more consistent predictions and better spatial mapping of LWC distribution.