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

摘要

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.