Accuracy and limitations for spectroscopic prediction of leaf traits in seasonally dry tropical environments

• Published in: Remote sensing of environment Vol. 244; p. 111828
• Main Authors: Streher, Annia Susin, Torres, Ricardo da Silva, Morellato, Leonor Patrícia Cerdeira, Silva, Thiago Sanna Freire
• Format: Journal Article
• Language: English
• Published: New York Elsevier Inc 01.07.2020; Elsevier BV
• Subjects: Adaptation; Assessments; Brazil; Cerrado; Chemical composition; Dry matter; Ecosystems; Forbs; Forest ecosystems; Forest vegetation; Forests; functional diversity; Grasses; herbs; LDMC; leaf dry matter content; leaf reflectance; Leaf spectroscopy; least squares; Leaves; LMA; metabolism; Model accuracy; Optical properties; Partial least squares regression (PLSR); Plant diversity; Plant functional traits, campo rupestre; Plant growth; prediction; Reflectance; reflectance spectroscopy; Regression analysis; Remote sensing; Shrubs; specific leaf weight; Spectra; Spectroscopy; Spectrum analysis; Temperate forests; trees; Tropical environment; Tropical environments; Vegetation; Woody plants; Cerrado Biome; Brazil; Leaf spectroscopy; Partial least squares regression (PLSR); LMA; Plant functional traits, campo rupestre; Cerrado; LDMC

Generalized assessments of the accuracy of spectroscopic estimates of ecologically important leaf traits such as leaf mass per area (LMA) and leaf dry matter content (LDMC) are still lacking for most ecosystems, and particularly for non-forested and/or seasonally dry tropical vegetation. Here, we te...