Scaling up tree diversity inventories across Amazonian ecosystems using field spectroscopy

Abstract

O_LISpecies identification in Amazonian forest inventories is challenging due to a shortage of taxonomists, high biodiversity, and morphological similarities leading to taxonomic errors. Near-infrared spectroscopy (NIRS) is a promising tool for improving species identification efficiency and reliability. C_LIO_LIThis study assessed the effectiveness of NIRS in discriminating against 26 abundant tree species across three Amazonian ecosystems: upland forest, white- sand ecosystems, and floodplain forest, using spectral data from different tree tissues--outer bark, inner bark, and fresh leaves. Each tissue was tested using Linear Discriminant Analysis (LDA) spectral models with two cross-validation methods: leave-one-out and 70/30 hold-out. C_LIO_LIResults showed high discrimination accuracy for all tissues and ecosystems. The general models achieved 86% accuracy for outer bark, 97% for inner bark and 98% for fresh leaves. The most informative spectral bands varied by tissue type: SWIR I (1300-1900 nm) for outer bark, and VIS (400-700 nm) + SWIR I (1300- 1900 nm) for inner bark and fresh leaves. A general model integrating species across ecosystems confirmed NIRS as an effective tool for in-field tree identification. These findings highlight the potential of VIS-NIR spectroscopy to Amazonian biodiversity inventories, contributing to more accurate species identification, refining forest management and conservation efforts. C_LI