Estimation of six leaf traits of East Asian forest tree species by leaf spectroscopy and partial least square regression

Abstract

© 2019 Elsevier Inc. To elucidate the potential of hyperspectral remote sensing for estimating the functional leaf traits in East Asian forests, we investigated the utility of leaf spectroscopy and partial least square regression (PLSR) models for 141 tree species distributed widely across cool temperate to tropical climate zones in the Pacific region. In 14 forests in Japan, Thailand, and Malaysia, leaf samples at various developmental stages (young, mature, and senescent) were collected from two plant functional types (deciduous and evergreen species). The target leaf traits were leaf mass per unit area and concentrations of nitrogen (N), carbon (C), total phenol, cellulose, and lignin. The leaf reflectance at visible-short wave infrared spectral reflectance from 400 nm to 2395 nm was measured at 3–10-nm intervals (351 bands) using a spectral radiometer. First, we compared the performance of the PLSR models in terms of dry mass (DM) -based concentration and leaf area (LA) –based concentration. Then, we investigated the applicability of the models based on the different plant functional types and leaf developmental stages of the training dataset. Finally, we evaluated the minimum number of spectral bands needed for stable performance of the PLSR model by changing the used bands in accordance with the variable importance of projection (VIP) and equal interval resampling. The coefficient of determination (R2) was higher and the normalized root mean square error (NRMSE) tended to be lower for all chemical trait concentrations expressed based on LA than for those based on DM, except for the case of leaf N. Plant functional type in the training data affected the applicability of the estimation model strongly. The estimation error increased when the functional type in the training dataset differed from that of the target tree species. In the PLSR model calibrated using datasets of evergreen tree species, the NRMSEs of the six traits were 8.0–12.6% for evergreen tree species but 10.4–21.1% for deciduous tree species. A similar trend was observed in the model calibrated for deciduous tree species. The model calibrated using both functional types showed intermediate accuracy for both types of tree species (NRMSE = 5.3–13.5%). The developmental stage of training data also affected the model performance, and the model calibrated using all of the stages showed better estimation accuracies for young and senescent leaves than the model calibrated from the data of mature leaves alone. The equal interval resampling provided better estimation accuracy than the case using the threshold of the VIP value although the model performance tended to be diminished with the reduction of input waveband in both methods. Using at least 104 bands with equal interval removal at a 20-nm interval confers similar performance of the PLSR model compared to the model with full wavebands. This study is the first to describe the potential of leaf spectroscopy for trait estimation of East Asian forest tree species, and our findings suggest that training datasets of typical functional types and varied developmental stages are important for estimation of leaf traits through the several biomes distributed widely in East Asia.